1
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Liu N, Pan JS, Liu G, Fu M, Kong Y, Hu P. A Multi-Objective Sine Cosine Algorithm Based on a Competitive Mechanism and Its Application in Engineering Design Problems. Biomimetics (Basel) 2024; 9:115. [PMID: 38392161 PMCID: PMC10887415 DOI: 10.3390/biomimetics9020115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 02/07/2024] [Accepted: 02/09/2024] [Indexed: 02/24/2024] Open
Abstract
There are a lot of multi-objective optimization problems (MOPs) in the real world, and many multi-objective evolutionary algorithms (MOEAs) have been presented to solve MOPs. However, obtaining non-dominated solutions that trade off convergence and diversity remains a major challenge for a MOEA. To solve this problem, this paper designs an efficient multi-objective sine cosine algorithm based on a competitive mechanism (CMOSCA). In the CMOSCA, the ranking relies on non-dominated sorting, and the crowding distance rank is utilized to choose the outstanding agents, which are employed to guide the evolution of the SCA. Furthermore, a competitive mechanism stemming from the shift-based density estimation approach is adopted to devise a new position updating operator for creating offspring agents. In each competition, two agents are randomly selected from the outstanding agents, and the winner of the competition is integrated into the position update scheme of the SCA. The performance of our proposed CMOSCA was first verified on three benchmark suites (i.e., DTLZ, WFG, and ZDT) with diversity characteristics and compared with several MOEAs. The experimental results indicated that the CMOSCA can obtain a Pareto-optimal front with better convergence and diversity. Finally, the CMOSCA was applied to deal with several engineering design problems taken from the literature, and the statistical results demonstrated that the CMOSCA is an efficient and effective approach for engineering design problems.
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Affiliation(s)
- Nengxian Liu
- College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China
| | - Jeng-Shyang Pan
- School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Genggeng Liu
- College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China
| | - Mingjian Fu
- College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China
| | - Yanyan Kong
- School of Information Science and Engineering, ZheJiang Sci-Tech University, Hangzhou 310018, China
| | - Pei Hu
- School of Computer and Software, Nanyang Institute of Technology, Nanyang 473004, China
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2
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Dao TK, Ngo TG, Pan JS, Nguyen TTT, Nguyen TT. Enhancing Path Planning Capabilities of Automated Guided Vehicles in Dynamic Environments: Multi-Objective PSO and Dynamic-Window Approach. Biomimetics (Basel) 2024; 9:35. [PMID: 38248609 PMCID: PMC10813721 DOI: 10.3390/biomimetics9010035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 01/23/2024] Open
Abstract
Automated guided vehicles (AGVs) are vital for optimizing the transport of material in modern industry. AGVs have been widely used in production, logistics, transportation, and commerce, enhancing productivity, lowering labor costs, improving energy efficiency, and ensuring safety. However, path planning for AGVs in complex and dynamic environments remains challenging due to the computation of obstacle avoidance and efficient transport. This study proposes a novel approach that combines multi-objective particle swarm optimization (MOPSO) and the dynamic-window approach (DWA) to enhance AGV path planning. Optimal AGV trajectories considering energy consumption, travel time, and collision avoidance were used to model the multi-objective functions for dealing with the outcome-feasible optimal solution. Empirical findings and results demonstrate the approach's effectiveness and efficiency, highlighting its potential for improving AGV navigation in real-world scenarios.
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Affiliation(s)
- Thi-Kien Dao
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China;
- School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
- Multimedia Communications Laboratory, University of Information Technology, Ho Chi Minh City 700000, Vietnam
- Vietnam National University, Ho Chi Minh City 700000, Vietnam
| | - Truong-Giang Ngo
- Faculty of Computer Science and Engineering, Thuyloi University, Hanoi 116705, Vietnam
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266510, China;
| | - Thi-Thanh-Tan Nguyen
- Faculty of Information Technology, Electric Power University, Hanoi 100000, Vietnam;
| | - Trong-The Nguyen
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China;
- School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
- Multimedia Communications Laboratory, University of Information Technology, Ho Chi Minh City 700000, Vietnam
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3
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Pan JS, Zhang XY, Chu SC, Wang RY, Lin BS. An Entropy-Balanced Orthogonal Learning Bamboo Forest Growth Optimization Algorithm with Quasi-Affine Transformation Evolutionary and Its Application in Capacitated Vehicle Routing Problem. Entropy (Basel) 2023; 25:1488. [PMID: 37998180 PMCID: PMC10670682 DOI: 10.3390/e25111488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 10/18/2023] [Accepted: 10/24/2023] [Indexed: 11/25/2023]
Abstract
The bamboo forest growth optimization (BFGO) algorithm combines the characteristics of the bamboo forest growth process with the optimization course of the algorithm. The algorithm performs well in dealing with optimization problems, but its exploitation ability is not outstanding. Therefore, a new heuristic algorithm named orthogonal learning quasi-affine transformation evolutionary bamboo forest growth optimization (OQBFGO) algorithm is proposed in this work. This algorithm combines the quasi-affine transformation evolution algorithm to expand the particle distribution range, a process of entropy increase that can significantly improve particle searchability. The algorithm also uses an orthogonal learning strategy to accurately aggregate particles from a chaotic state, which can be an entropy reduction process that can more accurately perform global development. OQBFGO algorithm, BFGO algorithm, quasi-affine transformation evolutionary bamboo growth optimization (QBFGO) algorithm, orthogonal learning bamboo growth optimization (OBFGO) algorithm, and three other mature algorithms are tested on the CEC2017 benchmark function. The experimental results show that the OQBFGO algorithm is superior to the above algorithms. Then, OQBFGO is used to solve the capacitated vehicle routing problem. The results show that OQBFGO can obtain better results than other algorithms.
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Affiliation(s)
- Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (J.-S.P.); (X.-Y.Z.); (R.-Y.W.)
- Department of Information Management, Chaoyang University of Technology, Taichung 41349, Taiwan
| | - Xin-Yi Zhang
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (J.-S.P.); (X.-Y.Z.); (R.-Y.W.)
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (J.-S.P.); (X.-Y.Z.); (R.-Y.W.)
| | - Ru-Yu Wang
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (J.-S.P.); (X.-Y.Z.); (R.-Y.W.)
| | - Bor-Shyh Lin
- Institute of Imaging and Biomedical Photonics, National Yang Ming Chiao Tung University, Tainan City 71150, Taiwan;
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4
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Chu SC, Shao ZY, Zhong N, Liu GG, Pan JS. An Enhanced Food Digestion Algorithm for Mobile Sensor Localization. Sensors (Basel) 2023; 23:7508. [PMID: 37687962 PMCID: PMC10490790 DOI: 10.3390/s23177508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 08/15/2023] [Accepted: 08/16/2023] [Indexed: 09/10/2023]
Abstract
Mobile sensors can extend the range of monitoring and overcome static sensors' limitations and are increasingly used in real-life applications. Since there can be significant errors in mobile sensor localization using the Monte Carlo Localization (MCL), this paper improves the food digestion algorithm (FDA). This paper applies the improved algorithm to the mobile sensor localization problem to reduce localization errors and improve localization accuracy. Firstly, this paper proposes three inter-group communication strategies to speed up the convergence of the algorithm based on the topology that exists between groups. Finally, the improved algorithm is applied to the mobile sensor localization problem, reducing the localization error and achieving good localization results.
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Affiliation(s)
- Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (S.-C.C.); (Z.-Y.S.)
- College of Science and Engineering, Flinders University, 1284 South Road, Tonsley, SA 5042, Australia
| | - Zhi-Yuan Shao
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (S.-C.C.); (Z.-Y.S.)
| | - Ning Zhong
- Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi 371-0816, Japan;
- International WIC Institute, Beijing University of Technology, Beijing 100124, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing 100124, China
| | - Geng-Geng Liu
- College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China;
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (S.-C.C.); (Z.-Y.S.)
- Department of Information Management, Chaoyang University of Technology, Taichung 41349, Taiwan
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5
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Cao Y, Kuai H, Liang P, Pan JS, Yan J, Zhong N. BNLoop-GAN: a multi-loop generative adversarial model on brain network learning to classify Alzheimer's disease. Front Neurosci 2023; 17:1202382. [PMID: 37424996 PMCID: PMC10326383 DOI: 10.3389/fnins.2023.1202382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 05/09/2023] [Indexed: 07/11/2023] Open
Abstract
Recent advancements in AI, big data analytics, and magnetic resonance imaging (MRI) have revolutionized the study of brain diseases such as Alzheimer's Disease (AD). However, most AI models used for neuroimaging classification tasks have limitations in their learning strategies, that is batch training without the incremental learning capability. To address such limitations, the systematic Brain Informatics methodology is reconsidered to realize evidence combination and fusion computing with multi-modal neuroimaging data through continuous learning. Specifically, we introduce the BNLoop-GAN (Loop-based Generative Adversarial Network for Brain Network) model, utilizing multiple techniques such as conditional generation, patch-based discrimination, and Wasserstein gradient penalty to learn the implicit distribution of brain networks. Moreover, a multiple-loop-learning algorithm is developed to combine evidence with better sample contribution ranking during training processes. The effectiveness of our approach is demonstrated through a case study on the classification of individuals with AD and healthy control groups using various experimental design strategies and multi-modal brain networks. The BNLoop-GAN model with multi-modal brain networks and multiple-loop-learning can improve classification performance.
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Affiliation(s)
- Yu Cao
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
| | - Hongzhi Kuai
- Faculty of Engineering, Maebashi Institute of Technology, Maebashi, Gunma, Japan
| | - Peipeng Liang
- School of Psychology and Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, China
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Jianzhuo Yan
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
| | - Ning Zhong
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
- Faculty of Engineering, Maebashi Institute of Technology, Maebashi, Gunma, Japan
- School of Psychology and Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, China
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6
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Liu FF, Chu SC, Hu CC, Watada J, Pan JS. An effective QUATRE algorithm based on reorganized mechanism and its application for parameter estimation in improved photovoltaic module. Heliyon 2023; 9:e16468. [PMID: 37416634 PMCID: PMC10320279 DOI: 10.1016/j.heliyon.2023.e16468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 05/09/2023] [Accepted: 05/17/2023] [Indexed: 07/08/2023] Open
Abstract
The traditional parameter estimation methods for photovoltaic (PV) module are strictly limited by the reference standards. On the basis of the double diode model (DDM), this paper proposes a modified PV module that is independent of the reference conditions and can be used for the transformation and reconfiguration of PV module. With respect to the issue of the slow convergence precision and the tendency to trap in the local extremum of the QUATRE algorithm, this research incorporates the QUATRE algorithm with recombination mechanism (RQUATRE) to tackle the problem of parameter estimation for the improved PV modules described above. Simulation data show that the RQUATRE wins 29, 29, 21, 17 and 15 times with the FMO, PIO, QUATRE, PSO and GWO algorithms on the CEC2017 test suite. In addition, in a modified PV module for the parameter extraction problem, the final experimental results achieved a value of 2.99 × 10-3 at RMSE, all better than the accuracy values of the compared algorithms. In the fitting process of IAE, the final values are also all less than 10%, which can satisfy the fitting needs.
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Affiliation(s)
- Fei-Fei Liu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China
- College of Science and Engineering, Flinders University, 1284 South Road, Tonsley, SA 5042, Australia
| | - Chia-Cheng Hu
- College of Artificial Intelligence, Yango University, Fuzhou, 350015, China
| | - Junzo Watada
- Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, 808-0135, Japan
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China
- Department of Information Management, Chaoyang University of Technology, Taichung, 413310, Taiwan
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7
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Zheng WM, Xu SL, Pan JS, Chai QW, Hu P. An Opposition-Based Learning Black Hole Algorithm for Localization of Mobile Sensor Network. Sensors (Basel) 2023; 23:s23094520. [PMID: 37177724 PMCID: PMC10181638 DOI: 10.3390/s23094520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 04/22/2023] [Accepted: 05/04/2023] [Indexed: 05/15/2023]
Abstract
The mobile node location method can find unknown nodes in real time and capture the movement trajectory of unknown nodes in time, which has attracted more and more attention from researchers. Due to their advantages of simplicity and efficiency, intelligent optimization algorithms are receiving increasing attention. Compared with other algorithms, the black hole algorithm has fewer parameters and a simple structure, which is more suitable for node location in wireless sensor networks. To address the problems of weak merit-seeking ability and slow convergence of the black hole algorithm, this paper proposed an opposition-based learning black hole (OBH) algorithm and utilized it to improve the accuracy of the mobile wireless sensor network (MWSN) localization. To verify the performance of the proposed algorithm, this paper tests it on the CEC2013 test function set. The results indicate that among the several algorithms tested, the OBH algorithm performed the best. In this paper, several optimization algorithms are applied to the Monte Carlo localization algorithm, and the experimental results show that the OBH algorithm can achieve the best optimization effect in advance.
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Affiliation(s)
- Wei-Min Zheng
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Shi-Lei Xu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Qing-Wei Chai
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Pei Hu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
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8
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Pan JS, Liu T, Yan B, Yang HM, Chu SC. A Lossless-Recovery Secret Distribution Scheme Based on QR Codes. Entropy (Basel) 2023; 25:e25040653. [PMID: 37190441 PMCID: PMC10137899 DOI: 10.3390/e25040653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 04/08/2023] [Accepted: 04/11/2023] [Indexed: 05/17/2023]
Abstract
The visual cryptography scheme (VCS) distributes a secret to several images that can enhance the secure transmission of that secret. Quick response (QR) codes are widespread. VCS can be used to improve their secure transmission. Some schemes recover QR codes with many errors. This paper uses a distribution mechanism to achieve the error-free recovery of QR codes. An error-correction codeword (ECC) is used to divide the QR code into different areas. Every area is a key, and they are distributed to n shares. The loss of any share will make the reconstructed QR code impossible to decode normally. Stacking all shares can recover the secret QR code losslessly. Based on some experiments, the proposed scheme is relatively safe. The proposed scheme can restore a secret QR code without errors, and it is effective and feasible.
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Affiliation(s)
- Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
- Department of Information Management, Chaoyang University of Technology, Taichung 413310, Taiwan
| | - Tao Liu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Bin Yan
- College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Hong-Mei Yang
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
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9
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Pan JS, Zhang LG, Chu SC, Shieh CS, Watada J. Surrogate-Assisted Hybrid Meta-Heuristic Algorithm with an Add-Point Strategy for a Wireless Sensor Network. Entropy (Basel) 2023; 25:317. [PMID: 36832683 PMCID: PMC9955869 DOI: 10.3390/e25020317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/30/2023] [Accepted: 02/04/2023] [Indexed: 06/18/2023]
Abstract
Meta-heuristic algorithms are widely used in complex problems that cannot be solved by traditional computing methods due to their powerful optimization capabilities. However, for high-complexity problems, the fitness function evaluation may take hours or even days to complete. The surrogate-assisted meta-heuristic algorithm effectively solves this kind of long solution time for the fitness function. Therefore, this paper proposes an efficient surrogate-assisted hybrid meta-heuristic algorithm by combining the surrogate-assisted model with gannet optimization algorithm (GOA) and the differential evolution (DE) algorithm, abbreviated as SAGD. We explicitly propose a new add-point strategy based on information from historical surrogate models, using information from historical surrogate models to allow the selection of better candidates for the evaluation of true fitness values and the local radial basis function (RBF) surrogate to model the landscape of the objective function. The control strategy selects two efficient meta-heuristic algorithms to predict the training model samples and perform updates. A generation-based optimal restart strategy is also incorporated in SAGD to select suitable samples to restart the meta-heuristic algorithm. We tested the SAGD algorithm using seven commonly used benchmark functions and the wireless sensor network (WSN) coverage problem. The results show that the SAGD algorithm performs well in solving expensive optimization problems.
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Affiliation(s)
- Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
- Department of Information Management, Chaoyang University of Technology, Taichung 41349, Taiwan
| | - Li-Gang Zhang
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Chin-Shiuh Shieh
- Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
| | - Junzo Watada
- Graduate School of Information, Production and Systems, Waseda University, Kitakyushu 808-0135, Japan
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10
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Pan JS, Yue L, Chu SC, Hu P, Yan B, Yang H. Binary Bamboo Forest Growth Optimization Algorithm for Feature Selection Problem. Entropy (Basel) 2023; 25:314. [PMID: 36832680 PMCID: PMC9955014 DOI: 10.3390/e25020314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 01/27/2023] [Accepted: 02/05/2023] [Indexed: 06/18/2023]
Abstract
Inspired by the bamboo growth process, Chu et al. proposed the Bamboo Forest Growth Optimization (BFGO) algorithm. It incorporates bamboo whip extension and bamboo shoot growth into the optimization process. It can be applied very well to classical engineering problems. However, binary values can only take 0 or 1, and for some binary optimization problems, the standard BFGO is not applicable. This paper firstly proposes a binary version of BFGO, called BBFGO. By analyzing the search space of BFGO under binary conditions, the new curve V-shaped and Taper-shaped transfer function for converting continuous values into binary BFGO is proposed for the first time. A long-mutation strategy with a new mutation approach is presented to solve the algorithmic stagnation problem. Binary BFGO and the long-mutation strategy with a new mutation are tested on 23 benchmark test functions. The experimental results show that binary BFGO achieves better results in solving the optimal values and convergence speed, and the variation strategy can significantly enhance the algorithm's performance. In terms of application, 12 data sets derived from the UCI machine learning repository are selected for feature-selection implementation and compared with the transfer functions used by BGWO-a, BPSO-TVMS and BQUATRE, which demonstrates binary BFGO algorithm's potential to explore the attribute space and choose the most significant features for classification issues.
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Affiliation(s)
- Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
- Department of Information Management, Chaoyang University of Technology, Taichung 41349, Taiwan
| | - Longkang Yue
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Pei Hu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Bin Yan
- College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Hongmei Yang
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
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11
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Pan JS, Zhang SQ, Chu SC, Yang HM, Yan B. Willow Catkin Optimization Algorithm Applied in the TDOA-FDOA Joint Location Problem. Entropy (Basel) 2023; 25:171. [PMID: 36673312 PMCID: PMC9858194 DOI: 10.3390/e25010171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 01/06/2023] [Accepted: 01/11/2023] [Indexed: 06/17/2023]
Abstract
The heuristic optimization algorithm is a popular optimization method for solving optimization problems. A novel meta-heuristic algorithm was proposed in this paper, which is called the Willow Catkin Optimization (WCO) algorithm. It mainly consists of two processes: spreading seeds and aggregating seeds. In the first process, WCO tries to make the seeds explore the solution space to find the local optimal solutions. In the second process, it works to develop each optimal local solution and find the optimal global solution. In the experimental section, the performance of WCO is tested with 30 test functions from CEC 2017. WCO was applied in the Time Difference of Arrival and Frequency Difference of Arrival (TDOA-FDOA) co-localization problem of moving nodes in Wireless Sensor Networks (WSNs). Experimental results show the performance and applicability of the WCO algorithm.
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Affiliation(s)
- Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
- Department of Information Management, Chaoyang University of Technology, Taichung 41349, Taiwan
| | - Si-Qi Zhang
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Hong-Mei Yang
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Bin Yan
- College of Electronics, Communication and Physics, Shandong University of Science and Technology, Qingdao 266590, China
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12
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Wu TY, Kong F, Wang L, Chen YC, Kumari S, Pan JS. Toward Smart Home Authentication Using PUF and Edge-Computing Paradigm. Sensors (Basel) 2022; 22:9174. [PMID: 36501875 PMCID: PMC9740584 DOI: 10.3390/s22239174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/17/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
The smart home is a crucial embodiment of the internet of things (IoT), which can facilitate users to access smart home services anytime and anywhere. Due to the limited resources of cloud computing, it cannot meet users' real-time needs. Therefore, edge computing emerges as the times require, providing users with better real-time access and storage. The application of edge computing in the smart home environment can enable users to enjoy smart home services. However, users and smart devices communicate through public channels, and malicious attackers may intercept information transmitted through public channels, resulting in user privacy disclosure. Therefore, it is a critical issue to protect the secure communication between users and smart devices in the smart home environment. Furthermore, authentication protocols in smart home environments also have some security challenges. In this paper, we propose an anonymous authentication protocol that applies edge computing to the smart home environment to protect communication security between entities. To protect the security of smart devices, we embed physical unclonable functions (PUF) into each smart device. Real-or-random model, informal security analysis, and ProVerif are adopted to verify the security of our protocol. Finally, we compare our protocol with existing protocols regarding security and performance. The comparison results demonstrate that our protocol has higher security and slightly better performance.
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Affiliation(s)
- Tsu-Yang Wu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Fangfang Kong
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Liyang Wang
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Yeh-Cheng Chen
- Department of Computer Science, University of California, Davis, CA 001313, USA
| | - Saru Kumari
- Department of Mathematics, Chaudhary Charan Singh University, Meerut 250004, India
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
- Department of Information Management, Chaoyang University of Technology, Taichung 41349, Taiwan
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13
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Pan JS, Hu P, Snášel V, Chu SC. A survey on binary metaheuristic algorithms and their engineering applications. Artif Intell Rev 2022; 56:6101-6167. [PMID: 36466763 PMCID: PMC9684803 DOI: 10.1007/s10462-022-10328-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, there were different implementations and applications of metaheuristic algorithms, or the study of engineering applications by different objective functions such as the single- and multi-objective problems of feature selection, scheduling, layout and engineering structure optimization. The article identifies current troubles and challenges by the conducted review, and discusses that novel binary algorithm, transfer function, benchmark function, time-consuming problem and application integration are need to be resolved in future.
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Affiliation(s)
- Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590 Shandong China
- Department of Information Management, Chaoyang University of Technology, Taichung, 413310 Taiwan
| | - Pei Hu
- Department of Information Management, Chaoyang University of Technology, Taichung, 413310 Taiwan
- School of Computer Science and Software Engineering, Nanyang Institute of Technology, Nanyang, 473004 Henan China
| | - Václav Snášel
- Faculty of Electrical Engineering and Computer Science, VŠB—Technical University of Ostrava, Ostrava, 70032 Moravskoslezský kraj Czech Republic
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590 Shandong China
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14
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Liu N, Pan JS, Chu SC, Hu P. A sinusoidal social learning swarm optimizer for large-scale optimization. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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15
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Han P, Han L, Yuan B, Pan JS, Shang J. A Parallelizable Task Offloading Model with Trajectory-Prediction for Mobile Edge Networks. Entropy (Basel) 2022; 24:1464. [PMID: 37420485 DOI: 10.3390/e24101464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 09/30/2022] [Accepted: 10/10/2022] [Indexed: 07/09/2023]
Abstract
As an emerging computing model, edge computing greatly expands the collaboration capabilities of the servers. It makes full use of the available resources around the users to quickly complete the task request coming from the terminal devices. Task offloading is a common solution for improving the efficiency of task execution on edge networks. However, the peculiarities of the edge networks, especially the random access of mobile devices, brings unpredictable challenges to the task offloading in a mobile edge network. In this paper, we propose a trajectory prediction model for moving targets in edge networks without users' historical paths which represents their habitual movement trajectory. We also put forward a mobility-aware parallelizable task offloading strategy based on a trajectory prediction model and parallel mechanisms of tasks. In our experiments, we compared the hit ratio of the prediction model, network bandwidth and task execution efficiency of the edge networks by using the EUA data set. Experimental results showed that our model is much better than random, non-position prediction parallel, non-parallel strategy-based position prediction. Where the task offloading hit rate is closed to the user's moving speed, when the speed is less 12.96 m/s, the hit rate can reach more than 80%. Meanwhile, we we also find that the bandwidth occupancy is significantly related to the degree of task parallelism and the number of services running on servers in the network. The parallel strategy can boost network bandwidth utilization by more than eight times when compared to a non-parallel policy as the number of parallel activities grows.
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Affiliation(s)
- Pu Han
- School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
- National Supercomputing Center in Zhengzhou, Zhengzhou University, Zhengzhou 450000, China
- Nanyang Institute of Technology, No.80, Changjiang Road, Nanyang 473000, China
| | - Lin Han
- National Supercomputing Center in Zhengzhou, Zhengzhou University, Zhengzhou 450000, China
| | - Bo Yuan
- School of Informatics, University of Leicester, Leicester LE1 7RH, UK
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
- Department of Information Management, Chaoyang University of Technology, Taichung 413310, Taiwan
| | - Jiandong Shang
- National Supercomputing Center in Zhengzhou, Zhengzhou University, Zhengzhou 450000, China
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16
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Liu N, Pan JS, Chu SC, Lai T. A surrogate-assisted bi-swarm evolutionary algorithm for expensive optimization. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04080-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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17
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Fu Z, Chu SC, Watada J, Hu CC, Pan JS. Software and hardware co-design and implementation of intelligent optimization algorithms. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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18
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Feng Q, Pan JS, Du ZG, Peng YJ, Chu SC. Multi-strategy improved parallel antlion algorithm and applied to feature selection. IFS 2022. [DOI: 10.3233/jifs-219315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Antlion Optimization Algorithm (ALO) is a promising bionic swarm intelligence algorithm, which has good robustness and convergence, but there are still many areas to be improved and modified. Aiming at the fact that the ALO algorithm is more likely to fall into the local optimum, proposes three strategies to improve the classic ALO algorithm in this paper. First of all, we adopt a parallel idea in the algorithm, through the communication strategy between groups based on Quantum-Behaved to enhance the diversity of the population. Secondly, we adopted two strategies, Opposition Learning, and Gaussian Mutation, to balance the performance of exploration and exploitation during the execution of the algorithm, further formed the MSALO algorithm. The CEC2013 Benchmark function is selected as the standard, and MSALO is compared with other intelligent optimization algorithms. The experimental results show that MSALO has stronger optimization performance compared with other intelligent algorithms. Besides, we applied MSALO to the practical scenarios of feature selection, and use SVM classifiers as training evaluators to improve the accuracy of feature extraction from high-dimensional data.
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Affiliation(s)
- Qing Feng
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Zhi-Gang Du
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Yan-jun Peng
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
- College of Science and Engineering, Flinders University, Clovelly Park, SA, Australia
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19
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Jia HD, Li W, Pan JS, Chai QW, Chu SC. Multi-group multi-verse optimizer for energy efficient for routing algorithm in wireless sensor network. IFS 2022. [DOI: 10.3233/jifs-219313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Wireless sensor network (WSN) is a network composed of a group of wireless sensors with limited energy. With the proliferation of sensor nodes, organization and management of sensor nodes become a challenging task. In this paper, a new topology is proposed to solve the routing problem in wireless sensor networks. Firstly, the sensor nodes are layered to avoid the ring path between cluster heads. Then the nodes of each layer are clustered to facilitate the integration of information and reduce energy dissipation. Moreover, we propose efficient multiverse optimization to mitigate the impact of local optimal solution prematurely and the population diversity declines prematurely. Extensive empirical studies on the CEC 2013 benchmark demonstrate the effectiveness of our new approach. The improved algorithm is further combined with the new topology to handle the routing problem in wireless sensor networks. The energy dissipation generated in routing is significantly lower than that of Multi-Verse Optimizer, Particle Swarm Optimization, and Parallel Particle Swarm Optimization in a wireless sensor network consisting of 5000 nodes.
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Affiliation(s)
- Han-Dong Jia
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Wei Li
- Faculty of the Built Environment, The University of New South Wales, NSW, Australia
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Qing-Wei Chai
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
- College of Science and Engineering, Flinders University, Clovelly Park, SA, Australia
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20
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Liang Q, Chu SC, Yang Q, Liang A, Pan JS. Multi-Group Gorilla Troops Optimizer with Multi-Strategies for 3D Node Localization of Wireless Sensor Networks. Sensors 2022; 22:s22114275. [PMID: 35684896 PMCID: PMC9185536 DOI: 10.3390/s22114275] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 05/23/2022] [Accepted: 05/31/2022] [Indexed: 01/27/2023]
Abstract
The localization problem of nodes in wireless sensor networks is often the focus of many researches. This paper proposes an opposition-based learning and parallel strategies Artificial Gorilla Troop Optimizer (OPGTO) for reducing the localization error. Opposition-based learning can expand the exploration space of the algorithm and significantly improve the global exploration ability of the algorithm. The parallel strategy divides the population into multiple groups for exploration, which effectively increases the diversity of the population. Based on this parallel strategy, we design communication strategies between groups for different types of optimization problems. To verify the optimized effect of the proposed OPGTO algorithm, it is tested on the CEC2013 benchmark function set and compared with Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA) and Artificial Gorilla Troops Optimizer (GTO). Experimental studies show that OPGTO has good optimization ability, especially on complex multimodal functions and combinatorial functions. Finally, we apply OPGTO algorithm to 3D localization of wireless sensor networks in the real terrain. Experimental results proved that OPGTO can effectively reduce the localization error based on Time Difference of Arrival (TDOA).
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Affiliation(s)
- Qingwei Liang
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (Q.L.); (S.-C.C.); (Q.Y.)
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (Q.L.); (S.-C.C.); (Q.Y.)
| | - Qingyong Yang
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (Q.L.); (S.-C.C.); (Q.Y.)
| | - Anhui Liang
- College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China;
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (Q.L.); (S.-C.C.); (Q.Y.)
- Department of Information Management, Chaoyang University of Technology, Taichung 413310, Taiwan
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China
- Correspondence:
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Wang GL, Chu SC, Tian AQ, Liu T, Pan JS. Improved Binary Grasshopper Optimization Algorithm for Feature Selection Problem. Entropy 2022; 24:e24060777. [PMID: 35741497 PMCID: PMC9223162 DOI: 10.3390/e24060777] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 05/24/2022] [Accepted: 05/29/2022] [Indexed: 11/16/2022]
Abstract
The migration and predation of grasshoppers inspire the grasshopper optimization algorithm (GOA). It can be applied to practical problems. The binary grasshopper optimization algorithm (BGOA) is used for binary problems. To improve the algorithm’s exploration capability and the solution’s quality, this paper modifies the step size in BGOA. The step size is expanded and three new transfer functions are proposed based on the improvement. To demonstrate the availability of the algorithm, a comparative experiment with BGOA, particle swarm optimization (PSO), and binary gray wolf optimizer (BGWO) is conducted. The improved algorithm is tested on 23 benchmark test functions. Wilcoxon rank-sum and Friedman tests are used to verify the algorithm’s validity. The results indicate that the optimized algorithm is significantly more excellent than others in most functions. In the aspect of the application, this paper selects 23 datasets of UCI for feature selection implementation. The improved algorithm yields higher accuracy and fewer features.
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Affiliation(s)
- Gui-Ling Wang
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (G.-L.W.); (S.-C.C.); (A.-Q.T.); (T.L.)
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (G.-L.W.); (S.-C.C.); (A.-Q.T.); (T.L.)
- College of Science and Engineering, Flinders University, Adelaide 5042, Australia
| | - Ai-Qing Tian
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (G.-L.W.); (S.-C.C.); (A.-Q.T.); (T.L.)
| | - Tao Liu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (G.-L.W.); (S.-C.C.); (A.-Q.T.); (T.L.)
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (G.-L.W.); (S.-C.C.); (A.-Q.T.); (T.L.)
- Department of Information Management, Chaoyang University of Technology, Taichung 413, China
- Correspondence:
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22
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Wang TT, Chu SC, Hu CC, Jia HD, Pan JS. Efficient Network Architecture Search Using Hybrid Optimizer. Entropy 2022; 24:e24050656. [PMID: 35626541 PMCID: PMC9140713 DOI: 10.3390/e24050656] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 04/21/2022] [Accepted: 04/29/2022] [Indexed: 02/06/2023]
Abstract
Manually designing a convolutional neural network (CNN) is an important deep learning method for solving the problem of image classification. However, most of the existing CNN structure designs consume a significant amount of time and computing resources. Over the years, the demand for neural architecture search (NAS) methods has been on the rise. Therefore, we propose a novel deep architecture generation model based on Aquila optimization (AO) and a genetic algorithm (GA). The main contributions of this paper are as follows: Firstly, a new encoding strategy representing the CNN coding structure is proposed, so that the evolutionary computing algorithm can be combined with CNN. Secondly, a new mechanism for updating location is proposed, which incorporates three typical operators from GA cleverly into the model we have designed so that the model can find the optimal solution in the limited search space. Thirdly, the proposed method can deal with the variable-length CNN structure by adding skip connections. Fourthly, combining traditional CNN layers and residual blocks and introducing a grouping strategy provides greater possibilities for searching for the optimal CNN structure. Additionally, we use two notable datasets, consisting of the MNIST and CIFAR-10 datasets for model evaluation. The experimental results show that our proposed model has good results in terms of search accuracy and time.
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Affiliation(s)
- Ting-Ting Wang
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (T.-T.W.); (H.-D.J.); (J.-S.P.)
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (T.-T.W.); (H.-D.J.); (J.-S.P.)
- Correspondence:
| | - Chia-Cheng Hu
- College of Artificial Intelligence, Yango University, Fuzhou 350015, China;
| | - Han-Dong Jia
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (T.-T.W.); (H.-D.J.); (J.-S.P.)
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (T.-T.W.); (H.-D.J.); (J.-S.P.)
- Department of Information Management, Chaoyang University of Technology, Taichung 41349, Taiwan
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23
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Hu P, Pan JS, Chu SC, Sun C. Multi-surrogate assisted binary particle swarm optimization algorithm and its application for feature selection. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.108736] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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24
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Lv JX, Yan LJ, Chu SC, Cai ZM, Pan JS, He XK, Xue JK. A new hybrid algorithm based on golden eagle optimizer and grey wolf optimizer for 3D path planning of multiple UAVs in power inspection. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07080-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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25
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Du ZG, Pan JS, Chu SC, Chiu YJ. Multi-group discrete symbiotic organisms search applied in traveling salesman problems. Soft comput 2022. [DOI: 10.1007/s00500-022-06862-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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26
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Pan JS, Liu N, Chu SC. A competitive mechanism based multi-objective differential evolution algorithm and its application in feature selection. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108582] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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27
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Song PC, Pan JS, Chu SC. Corrigendum to “A parallel compact cuckoo search algorithm for three-dimensional path planning” [Appl. Soft Comput. 94 (2020) 106443]. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2020.106710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Chu SC, Xu XW, Yang SY, Pan JS. Parallel fish migration optimization with compact technology based on memory principle for wireless sensor networks. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.108124] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Wang X, Chu SC, Snášel V, Kong L, Pan JS, Shehadeh HA. A two-phase quasi-affine transformation evolution with feedback for parameter identification of photovoltaic models. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Abstract
Spectrum has now become a scarce resource due to the continuous development of wireless communication technology. Cognitive radio technology is considered to be a new method to solve the shortage of spectrum resources. The spectrum allocation model of cognitive radio can effectively avoid the waste of spectrum resources. A novel binary version of slime mould algorithm is proposed for the spectrum allocation model to solve the spectrum allocation scheme. In addition, adding unselected factors strategy can make the approach find a better solution. Compared with other algorithms, the novel binary slime mould algorithm and the strategy of adding unselected factors proposed in this paper have a good performance in spectrum allocation. The resulting spectrum allocation scheme can achieve efficient use of network resources.
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Affiliation(s)
- Ling Li
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590 China
| | - Tien-Szu Pan
- Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan
| | - Xiao-Xue Sun
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590 China
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590 China
- College of Science and Engineering, Flinders University, 1284 South Road, Clovelly Park, 5042 SA Australia
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590 China
- Department of Information Management, Chaoyang University of Technology, Taichung, Taiwan
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Shan J, Pan JS, Chang CK, Chu SC, Zheng SG. A distributed parallel firefly algorithm with communication strategies and its application for the control of variable pitch wind turbine. ISA Trans 2021; 115:79-94. [PMID: 33485629 DOI: 10.1016/j.isatra.2021.01.026] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 01/12/2021] [Accepted: 01/12/2021] [Indexed: 06/12/2023]
Abstract
Firefly algorithm (FA) is a meta-heuristic optimization algorithm inspired by nature. Due to its superior performance, it has been widely used in real life. However, it also has some shortcomings in some optimization cases, such as low solution accuracy and slow solution speed. Therefore, in this paper, distributed parallel firefly algorithm (DPFA) with four communication strategies is presented to improve these shortcomings. The distributed parallel technique is implanted to divide the initial fireflies into several subgroups, and exchange the information based on communication strategies among subgroups after the fixed iteration. The communication strategies include the maximum of the same group, the average of the same group, the maximum of different groups and the average of different groups. For verifying its performance, this paper compared DPFA with famous optimization algorithms, and experimental results show that DPFA has stronger competitiveness under the test suite of CEC2013. Furthermore, the proposed DPFA is also applied to the PID parameter tuning of variable pitch wind turbine, and conducted experiments show that DPFA outperforms other algorithms. It can smooth the power output and reduce the impact on the power grid when the wind speed fluctuates.
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Affiliation(s)
- Jie Shan
- School of Electronic Engineering and Physics, Fujian University of Technology, Fuchou, Fujian, China
| | - Jeng-Shyang Pan
- School of Electronic Engineering and Physics, Fujian University of Technology, Fuchou, Fujian, China; College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; Department of Information Management, Chaoyang University of Technology, Taiwan.
| | - Cheng-Kuo Chang
- School of Electronic Engineering and Physics, Fujian University of Technology, Fuchou, Fujian, China
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; College of Science and Engineering, Flinders University, 1284 South Road, Clovelly Park SA 5042, Australia
| | - Shi-Guang Zheng
- School of Electronic Engineering and Physics, Fujian University of Technology, Fuchou, Fujian, China
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Cao X, Pan JS, Wang Z, Sun Z, Ul Haq A, Deng W, Yang S. Application of generated mask method based on Mask R-CNN in classification and detection of melanoma. Comput Methods Programs Biomed 2021; 207:106174. [PMID: 34058631 DOI: 10.1016/j.cmpb.2021.106174] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 05/05/2021] [Indexed: 06/12/2023]
Abstract
OBJECTIVE Melanoma is a type of malignant skin cancer with high mortality, and its incidence is increasing rapidly in recent years. At present, the best treatment is surgical resection after early diagnosis. However, due to the high visual similarity between melanoma and benign melanocytic nevus, coupled with the scarcity and imbalance of data, traditional methods are difficult to achieve good recognition and detection results. Similarly, many machine learning methods have been applied to the task of skin disease detection and classification. However, the accuracy and sensitivity of the experiments are still not satisfactory. Therefore, this paper proposed a method to identify melanoma more efficiently and accurately. METHOD We implemented a Mixed Skin Lesion Picture Generate method based on Mask R-CNN (MSLP-MR) to solve the problem of data imbalance. Besides, we designed a melanoma detection framework of Mask-DenseNet+ based on MSLP-MR. This method used Mask R-CNN to introduce the method of mask segmentation, and combined with the idea of ensemble learning to integrate multiple classifiers for weighted prediction. Compared with the ablation experiments, the accuracy, sensitivity and AUC of the proposed network classification are improved by 2.56%, 29.33% and 0.0345. RESULT The experimental results on the ISIC dataset shown that the accuracy of the algorithm is 90.61%, the sensitivity reaches 78.00%, which is higher than the original methods; the specificity reaches 93.43%; and the AUC reaches 0.9502. CONCLUSION The method is feasible and effective, and achieves the preliminary goal of melanoma detection. It is greatly improved the detection accuracy and reached the level of visual diagnosis of doctors.
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Affiliation(s)
- Xingmei Cao
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Shandong 250100, China
| | - Zhengdi Wang
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Zhonghai Sun
- Xiamen Information Group Big Data Operation Co., Ltd, China
| | - Anwar Ul Haq
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Wenyu Deng
- School of Informatics, Xiamen University, Xiamen 361005, China
| | - Shuangyuan Yang
- School of Informatics, Xiamen University, Xiamen 361005, China.
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Shao ZY, Pan JS, Hu P, Chu SC. Equilibrium optimizer of interswarm interactive learning strategy. ENTERP INF SYST-UK 2021. [DOI: 10.1080/17517575.2021.1949636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Zhi-Yuan Shao
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Pei Hu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
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Abstract
AbstractThis work proposes a population evolution algorithm to deal with optimization problems based on the evolution characteristics of the Phasmatodea (stick insect) population, called the Phasmatodea population evolution algorithm (PPE). The PPE imitates the characteristics of convergent evolution, path dependence, population growth and competition in the evolution of the stick insect population in nature. The stick insect population tends to be the nearest dominant population in the evolution process, and the favorable evolution trend is more likely to be inherited by the next generation. This work combines population growth and competition models to achieve the above process. The implemented PPE has been tested and analyzed on 30 benchmark functions, and it has better performance than similar algorithms. This work uses several engineering optimization problems to test the algorithm and obtains good results.
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Fan F, Chu SC, Pan JS, Lin C, Zhao H. An optimized machine learning technology scheme and its application in fault detection in wireless sensor networks. J Appl Stat 2021; 50:592-609. [PMID: 36819085 PMCID: PMC9930809 DOI: 10.1080/02664763.2021.1929089] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Aiming at the problem of fault detection in data collection in wireless sensor networks, this paper combines evolutionary computing and machine learning to propose a productive technical solution. We choose the classical particle swarm optimization (PSO) and improve it, including the introduction of a biological population model to control the population size, and the addition of a parallel mechanism for further tuning. The proposed RS-PPSO algorithm was successfully used to optimize the initial weights and biases of back propagation neural network (BPNN), shortening the training time and raising the prediction accuracy. Wireless sensor networks (WSN) has become the key supporting platform of Internet of Things (IoT). The correctness of the data collected by the sensor nodes has a great influence on the reliability, real-time performance and energy saving of the entire network. The optimized machine learning technology scheme given in this paper can effectively identify the fault data, so as to ensure the effective operation of WSN.
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Affiliation(s)
- Fang Fan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, People’s Republic of China
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, People’s Republic of China
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, People’s Republic of China, Jeng-Shyang Pan
| | - Chuang Lin
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People’s Republic of China
| | - Huiqi Zhao
- College of Intelligent Equipment, Shandong University of Science and Technology, Taian, People’s Republic of China
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Liu H, Liu J, Li J, Pan JS, Yu X. DL-MRI: A Unified Framework of Deep Learning-Based MRI Super Resolution. J Healthc Eng 2021; 2021:5594649. [PMID: 33897991 PMCID: PMC8052167 DOI: 10.1155/2021/5594649] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 03/22/2021] [Accepted: 03/30/2021] [Indexed: 11/18/2022]
Abstract
Magnetic resonance imaging (MRI) is widely used in the detection and diagnosis of diseases. High-resolution MR images will help doctors to locate lesions and diagnose diseases. However, the acquisition of high-resolution MR images requires high magnetic field intensity and long scanning time, which will bring discomfort to patients and easily introduce motion artifacts, resulting in image quality degradation. Therefore, the resolution of hardware imaging has reached its limit. Based on this situation, a unified framework based on deep learning super resolution is proposed to transfer state-of-the-art deep learning methods of natural images to MRI super resolution. Compared with the traditional image super-resolution method, the deep learning super-resolution method has stronger feature extraction and characterization ability, can learn prior knowledge from a large number of sample data, and has a more stable and excellent image reconstruction effect. We propose a unified framework of deep learning -based MRI super resolution, which has five current deep learning methods with the best super-resolution effect. In addition, a high-low resolution MR image dataset with the scales of ×2, ×3, and ×4 was constructed, covering 4 parts of the skull, knee, breast, and head and neck. Experimental results show that the proposed unified framework of deep learning super resolution has a better reconstruction effect on the data than traditional methods and provides a standard dataset and experimental benchmark for the application of deep learning super resolution in MR images.
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Affiliation(s)
- Huanyu Liu
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
- Center of AI Perception, AI Research Institute, Harbin Institute of Technology, Harbin 150001, China
| | - Jiaqi Liu
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
- Center of AI Perception, AI Research Institute, Harbin Institute of Technology, Harbin 150001, China
| | - Junbao Li
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
- Center of AI Perception, AI Research Institute, Harbin Institute of Technology, Harbin 150001, China
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Xiaqiong Yu
- 32021 Troops of the PLA, Beijing 100094, China
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Weng S, Tan W, Ou B, Pan JS. Reversible data hiding method for multi-histogram point selection based on improved crisscross optimization algorithm. Inf Sci (N Y) 2021. [DOI: 10.1016/j.ins.2020.10.063] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Abstract
In this study, an automatic pennation angle measuring approach based on deep learning is proposed. Firstly, the Local Radon Transform (LRT) is used to detect the superficial and deep aponeuroses on the ultrasound image. Secondly, a reference line are introduced between the deep and superficial aponeuroses to assist the detection of the orientation of muscle fibers. The Deep Residual Networks (Resnets) are used to judge the relative orientation of the reference line and muscle fibers. Then, reference line is revised until the line is parallel to the orientation of the muscle fibers. Finally, the pennation angle is obtained according to the direction of the detected aponeuroses and the muscle fibers. The angle detected by our proposed method differs by about 1° from the angle manually labeled. With a CPU, the average inference time for a single image of the muscle fibers with the proposed method is around 1.6 s, compared to 0.47 s for one of the image of a sequential image sequence. Experimental results show that the proposed method can achieve accurate and robust measurements of pennation angle.
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Affiliation(s)
- Weimin Zheng
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Shangkun Liu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Qing-Wei Chai
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
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Liu N, Pan JS, Sun C, Chu SC. An efficient surrogate-assisted quasi-affine transformation evolutionary algorithm for expensive optimization problems. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.106418] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Affiliation(s)
- Trong-The Nguyen
- Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, Fujian, China
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fujian, China
- Department of Information Technology, University of Manage and Technology, Haiphong, Vietnam
| | - Yu Qiao
- Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, Fujian, China
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fujian, China
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Kuo-Chi Chang
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fujian, China
| | - Xingsi Xue
- Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, Fujian, China
| | - Thi-Kien Dao
- Intelligent Information Processing Research Center, Fujian University of Technology, Fuzhou, Fujian, China
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fujian, China
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Affiliation(s)
- Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China
| | - Cheng Yang
- Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China
| | - Fanjia Meng
- Guanzhuang Central Primary School of Zhangqiu District, Jinan, China
| | - Yuxin Chen
- Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China
| | - Zhenyu Meng
- Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China
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Chu SC, Chen Y, Meng F, Yang C, Pan JS, Meng Z. Internal search of the evolution matrix in QUasi-Affine TRansformation Evolution (QUATRE) algorithm. IFS 2020. [DOI: 10.3233/jifs-179656] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Yuxin Chen
- Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China
- Fujian Provincial Key Lab of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China
| | - Fanjia Meng
- Guanzhuang Central Primary School of Zhangqiu District, Jinan, China
| | - Chen Yang
- Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China
- Fujian Provincial Key Lab of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
- Fujian Provincial Key Lab of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China
| | - Zhenyu Meng
- Institute of Artificial Intelligence, Fujian University of Technology, Fuzhou, China
- Fujian Provincial Key Lab of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China
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Pan JS, Chai QW, Chu SC, Wu N. 3-D Terrain Node Coverage of Wireless Sensor Network Using Enhanced Black Hole Algorithm. Sensors (Basel) 2020; 20:s20082411. [PMID: 32340324 PMCID: PMC7219582 DOI: 10.3390/s20082411] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 04/19/2020] [Accepted: 04/21/2020] [Indexed: 11/16/2022]
Abstract
In this paper, a new intelligent computing algorithm named Enhanced Black Hole (EBH) is proposed to which the mutation operation and weight factor are applied. In EBH, several elites are taken as role models instead of only one in the original Black Hole (BH) algorithm. The performance of the EBH algorithm is verified by the CEC 2013 test suit, and shows better results than the original BH. In addition, the EBH and other celebrated algorithms can be used to solve node coverage problems of Wireless Sensor Network (WSN) in 3-D terrain with satisfactory performance.
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Affiliation(s)
- Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (J.-S.P.); (Q.-W.C.)
| | - Qing-Wei Chai
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (J.-S.P.); (Q.-W.C.)
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (J.-S.P.); (Q.-W.C.)
- Correspondence:
| | - Ning Wu
- School of Electronic and Information Engineering, Beibu Gulf University, Qinzhou 535011, China;
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Zhang F, Wu TY, Pan JS, Ding G, Li Z. Human motion recognition based on SVM in VR art media interaction environment. Hum Cent Comput Inf Sci 2019. [DOI: 10.1186/s13673-019-0203-8] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Abstract
In order to solve the problem of human motion recognition in multimedia interaction scenarios in virtual reality environment, a motion classification and recognition algorithm based on linear decision and support vector machine (SVM) is proposed. Firstly, the kernel function is introduced into the linear discriminant analysis for nonlinear projection to map the training samples into a high-dimensional subspace to obtain the best classification feature vector, which effectively solves the nonlinear problem and expands the sample difference. The genetic algorithm is used to realize the parameter search optimization of SVM, which makes full use of the advantages of genetic algorithm in multi-dimensional space optimization. The test results show that compared with other classification recognition algorithms, the proposed method has a good classification effect on multiple performance indicators of human motion recognition and has higher recognition accuracy and better robustness.
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Liu X, Li JB, Pan JS. Feature Point Matching Based on Distinct Wavelength Phase Congruency and Log-Gabor Filters in Infrared and Visible Images. Sensors (Basel) 2019; 19:s19194244. [PMID: 31569596 PMCID: PMC6806253 DOI: 10.3390/s19194244] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 09/20/2019] [Accepted: 09/24/2019] [Indexed: 11/17/2022]
Abstract
Infrared and visible image matching methods have been rising in popularity with the emergence of more kinds of sensors, which provide more applications in visual navigation, precision guidance, image fusion, and medical image analysis. In such applications, image matching is utilized for location, fusion, image analysis, and so on. In this paper, an infrared and visible image matching approach, based on distinct wavelength phase congruency (DWPC) and log-Gabor filters, is proposed. Furthermore, this method is modified for non-linear image matching with different physical wavelengths. Phase congruency (PC) theory is utilized to obtain PC images with intrinsic and affluent image features for images containing complex intensity changes or noise. Then, the maximum and minimum moments of the PC images are computed to obtain the corners in the matched images. In order to obtain the descriptors, log-Gabor filters are utilized and overlapping subregions are extracted in a neighborhood of certain pixels. In order to improve the accuracy of the algorithm, the moments of PCs in the original image and a Gaussian smoothed image are combined to detect the corners. Meanwhile, it is improper that the two matched images have the same PC wavelengths, due to the images having different physical wavelengths. Thus, in the experiment, the wavelength of the PC is changed for different physical wavelengths. For realistic application, BiDimRegression method is proposed to compute the similarity between two points set in infrared and visible images. The proposed approach is evaluated on four data sets with 237 pairs of visible and infrared images, and its performance is compared with state-of-the-art approaches: the edge-oriented histogram descriptor (EHD), phase congruency edge-oriented histogram descriptor (PCEHD), and log-Gabor histogram descriptor (LGHD) algorithms. The experimental results indicate that the accuracy rate of the proposed approach is 50% higher than the traditional approaches in infrared and visible images.
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Affiliation(s)
- Xiaomin Liu
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China.
- Information and Electronic Technology Institute, Jiamusi University, Jiamusi 154002, China.
| | - Jun-Bao Li
- School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China.
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266510, China.
- Fujian Provincial Key Laboratory of Big Data Minning and Applications, Fujian University of Technology, Fuzhou 350118, China.
- College of Informatics, Chaoyang University of Science and Technology, Taichung 413, Taiwan.
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Liu N, Pan JS, Wang J, Nguyen TT. An Adaptation Multi-Group Quasi-Affine Transformation Evolutionary Algorithm for Global Optimization and Its Application in Node Localization in Wireless Sensor Networks. Sensors (Basel) 2019; 19:s19194112. [PMID: 31547580 PMCID: PMC6806068 DOI: 10.3390/s19194112] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2019] [Revised: 09/20/2019] [Accepted: 09/21/2019] [Indexed: 11/30/2022]
Abstract
Developing metaheuristic algorithms has been paid more recent attention from researchers and scholars to address the optimization problems in many fields of studies. This paper proposes a novel adaptation of the multi-group quasi-affine transformation evolutionary algorithm for global optimization. Enhanced population diversity for adaptation multi-group quasi-affine transformation evolutionary algorithm is implemented by randomly dividing its population into three groups. Each group adopts a mutation strategy differently for improving the efficiency of the algorithm. The scale factor F of mutations is updated adaptively during the search process with the different policies along with proper parameter to make a better trade-off between exploration and exploitation capability. In the experimental section, the CEC2013 test suite and the node localization in wireless sensor networks were used to verify the performance of the proposed algorithm. The experimental results are compared results with three quasi-affine transformation evolutionary algorithm variants, two different evolution variants, and two particle swarm optimization variants show that the proposed adaptation multi-group quasi-affine transformation evolutionary algorithm outperforms the competition algorithms. Moreover, analyzed results of the applied adaptation multi-group quasi-affine transformation evolutionary for node localization in wireless sensor networks showed that the proposed method produces higher localization accuracy than the other competing algorithms.
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Affiliation(s)
- Nengxian Liu
- College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China.
| | - Jeng-Shyang Pan
- College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China.
- Fujian Provincial Key Lab of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China.
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
| | - Jin Wang
- Fujian Provincial Key Lab of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China.
- Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha 410000, China.
| | - Trong-The Nguyen
- Fujian Provincial Key Lab of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China.
- Department of Information Technology, University of Manage and Technology, Haiphong 180000, Vietnam.
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Meng Z, Pan JS, Tseng KK. PaDE: An enhanced Differential Evolution algorithm with novel control parameter adaptation schemes for numerical optimization. Knowl Based Syst 2019. [DOI: 10.1016/j.knosys.2019.01.006] [Citation(s) in RCA: 139] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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