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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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Zheng W, Pang S, Liu N, Chai Q, Xu L. A Compact Snake Optimization Algorithm in the Application of WKNN Fingerprint Localization. Sensors (Basel) 2023; 23:6282. [PMID: 37514575 PMCID: PMC10383412 DOI: 10.3390/s23146282] [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: 06/03/2023] [Revised: 06/29/2023] [Accepted: 06/30/2023] [Indexed: 07/30/2023]
Abstract
Indoor localization has broad application prospects, but accurately obtaining the location of test points (TPs) in narrow indoor spaces is a challenge. The weighted K-nearest neighbor algorithm (WKNN) is a powerful localization algorithm that can improve the localization accuracy of TPs. In recent years, with the rapid development of metaheuristic algorithms, it has shown efficiency in solving complex optimization problems. The main research purpose of this article is to study how to use metaheuristic algorithms to improve indoor positioning accuracy and verify the effectiveness of heuristic algorithms in indoor positioning. This paper presents a new algorithm called compact snake optimization (cSO). The novel algorithm introduces a compact strategy to the snake optimization (SO) algorithm, which ensures optimal performance in situations with limited computing and memory resources. The performance of cSO is evaluated on 28 test functions of CEC2013 and compared with several intelligent computing algorithms. The results demonstrate that cSO outperforms these algorithms. Furthermore, we combine the cSO algorithm with WKNN fingerprint positioning and RSSI positioning. The simulation experiments demonstrate that the cSO algorithm can effectively reduce positioning errors.
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Affiliation(s)
- Weimin Zheng
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Senyuan Pang
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Ning Liu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Qingwei Chai
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Lindong Xu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
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Zhu J, Liu J, Chen Y, Xue X, Sun S. Binary Restructuring Particle Swarm Optimization and Its Application. Biomimetics (Basel) 2023; 8:266. [PMID: 37366861 DOI: 10.3390/biomimetics8020266] [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: 05/16/2023] [Revised: 06/14/2023] [Accepted: 06/15/2023] [Indexed: 06/28/2023] Open
Abstract
Restructuring Particle Swarm Optimization (RPSO) algorithm has been developed as an intelligent approach based on the linear system theory of particle swarm optimization (PSO). It streamlines the flow of the PSO algorithm, specifically targeting continuous optimization problems. In order to adapt RPSO for solving discrete optimization problems, this paper proposes the binary Restructuring Particle Swarm Optimization (BRPSO) algorithm. Unlike other binary metaheuristic algorithms, BRPSO does not utilize the transfer function. The particle updating process in BRPSO relies solely on comparison results between values derived from the position updating formula and a random number. Additionally, a novel perturbation term is incorporated into the position updating formula of BRPSO. Notably, BRPSO requires fewer parameters and exhibits high exploration capability during the early stages. To evaluate the efficacy of BRPSO, comprehensive experiments are conducted by comparing it against four peer algorithms in the context of feature selection problems. The experimental results highlight the competitive nature of BRPSO in terms of both classification accuracy and the number of selected features.
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Affiliation(s)
- Jian Zhu
- School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fuzhou 350118, China
| | - Jianhua Liu
- School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fuzhou 350118, China
| | - Yuxiang Chen
- School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fuzhou 350118, China
| | - Xingsi Xue
- School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fuzhou 350118, China
| | - Shuihua Sun
- School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fuzhou 350118, China
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Guo H, Ma J, Wang R, Zhou Y. Feature library-assisted surrogate model for evolutionary wrapper-based feature selection and classification. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110241] [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: 03/30/2023]
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5
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Lin L, Wang J, Gao S, Zhang Z. Deep Generation Network for Multivariate Spatio-temporal Data Based on Separated Attention. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.03.062] [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: 03/16/2023]
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6
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Atban F, Ekinci E, Garip Z. Traditional machine learning algorithms for breast cancer image classification with optimized deep features. Biomed Signal Process Control 2023; 81:104534. [DOI: 10.1016/j.bspc.2022.104534] [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: 12/24/2022]
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Wang ZJ, Yang Q, Zhang YH, Chen SH, Wang YG. Superiority combination learning distributed particle swarm optimization for large-scale optimization. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110101] [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: 02/18/2023]
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Zhang M, Wang JS, Hou JN, Song HM, Li XD, Guo FJ. RG-NBEO: a ReliefF guided novel binary equilibrium optimizer with opposition-based S-shaped and V-shaped transfer functions for feature selection. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10333-y] [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: 11/29/2022]
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Liu S, Wang H, Yao W. A surrogate-assisted evolutionary algorithm with hypervolume triggered fidelity adjustment for noisy multiobjective integer programming. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109263] [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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Liu Y, Zheng WM, Liu S, Chai QW. Gaussian-Based Adaptive Fish Migration Optimization Applied to Optimization Localization Error of Mobile Sensor Networks. Entropy (Basel) 2022; 24:1109. [PMID: 36010773 PMCID: PMC9407049 DOI: 10.3390/e24081109] [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: 07/03/2022] [Revised: 08/05/2022] [Accepted: 08/06/2022] [Indexed: 06/15/2023]
Abstract
Location information is the primary feature of wireless sensor networks, and it is more critical for Mobile Wireless Sensor Networks (MWSN) to monitor specific targets. How to improve the localization accuracy is a challenging problem for researchers. In this paper, the Gaussian probability distribution model is applied to randomize the individual during the migration of the Adaptive Fish Migration Optimization (AFMO) algorithm. The performance of the novel algorithm is verified by the CEC 2013 test suit, and the result is compared with other famous heuristic algorithms. Compared to other well-known heuristics, the new algorithm achieves the best results in almost 21 of all 28 test functions. In addition, the novel algorithm significantly reduces the localization error of MWSN, the simulation results show that the accuracy of the new algorithm is more than 5% higher than that of other heuristic algorithms in terms of mobile sensor node positioning, and more than 100% higher than that without the heuristic algorithm.
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Affiliation(s)
- Yong Liu
- College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
- Laboratory for Marine Geology, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
| | - Wei-Min Zheng
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
| | - Shangkun Liu
- 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
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Xue Y, Cai X, Neri F. A multi-objective evolutionary algorithm with interval based initialization and self-adaptive crossover operator for large-scale feature selection in classification. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109420] [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: 01/05/2023]
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