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Liu Y, Liang Y. Satin bowerbird optimizer-neural network for approximating the capacity of CFST columns under compression. Sci Rep 2024; 14:8342. [PMID: 38594336 PMCID: PMC11004027 DOI: 10.1038/s41598-024-58756-7] [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: 12/16/2023] [Accepted: 04/02/2024] [Indexed: 04/11/2024] Open
Abstract
Concrete-filled steel tube columns (CFSTCs) are important elements in the construction sector and predictive analysis of their behavior is essential. Recent works have revealed the potential of metaheuristic-assisted approximators for this purpose. The main idea of this paper, therefore, is to introduce a novel integrative model for appraising the axial compression capacity (Pu) of CFSTCs. The proposed model represents an artificial neural network (ANN) supervised by satin bowerbird optimizer (SBO). In other words, this metaheuristic algorithm trains the ANN optimally to find the best contribution of input parameters to the Pu. In this sense, column length and the compressive strength of concrete, as well as the characteristics of the steel tube (i.e., diameter, thickness, yield stress, and ultimate stress), are considered input data. The prediction results are compared to five ANNs supervised by backtracking search algorithm (BSA), earthworm optimization algorithm (EWA), social spider algorithm (SOSA), salp swarm algorithm (SSA), and wind-driven optimization. Evaluating various accuracy indicators showed that the proposed model surpassed all of them in both learning and reproducing the Pu pattern. The obtained values of mean absolute percentage error of the SBO-ANN was 2.3082% versus 4.3821%, 17.4724%, 15.7898%, 4.2317%, and 3.6884% for the BSA-ANN, EWA-ANN, SOSA-ANN, SSA-ANN and WDA-ANN, respectively. The higher accuracy of the SBO-ANN against several hybrid models from earlier literature was also deduced. Moreover, the outcomes of principal component analysis on the dataset showed that the yield stress, diameter, and ultimate stress of the steel tube are the three most important factors in Pu prediction. A predictive formula is finally derived from the optimized SBO-ANN by extracting and organizing the weights and biases of the ANN. Owing to the accurate estimation shown by this model, the derived formula can reliably predict the Pu of concrete-filled steel tube columns.
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Affiliation(s)
- Yuzhen Liu
- Bim School of Technology and Industry, Changchun Institute of Technology, Changchun, 130012, Jilin, China
| | - Yan Liang
- Infrastructure Logistics Office, Jilin Engineering Normal University, Changchun, 130012, Jilin, China.
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2
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Liu Y, Liang Y. Integrated machine learning for modeling bearing capacity of shallow foundations. Sci Rep 2024; 14:8319. [PMID: 38594332 PMCID: PMC11004173 DOI: 10.1038/s41598-024-58534-5] [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: 11/21/2023] [Accepted: 04/01/2024] [Indexed: 04/11/2024] Open
Abstract
Analyzing the stability of footings is a significant step in civil/geotechnical engineering projects. In this work, two novel predictive tools are suggested based on an artificial neural network (ANN) to analyze the bearing capacity of a footing installed on a two-layered soil mass. To this end, backtracking search algorithm (BSA) and equilibrium optimizer (EO) are employed to train the ANN for approximating the stability value (SV) of the system. After executing a set of finite element analyses, the settlement values lower/higher than 5 cm are considered to indicate the stability/failure of the system. The results demonstrated the efficiency of these algorithms in fulfilling the assigned task. In detail, the training error of the ANN (in terms of root mean square error-RMSE)) dropped from 0.3585 to 0.3165 (11.72%) and 0.2959 (17.46%) by applying the BSA and EO, respectively. Moreover, the prediction accuracy of the ANN climbed from 93.7 to 94.3% and 94.1% (in terms of area under the receiving operating characteristics curve-AUROC). A comparison between the elite complexities of these algorithms showed that the EO enjoys a larger accuracy, while BSA is a more time-effective optimizer. Lastly, an explicit mathematical formula is derived from the EO-ANN model to be conveniently used in predicting the SV.
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Affiliation(s)
- Yuzhen Liu
- Bim School of Technology and Industry, Changchun Institute of Technology, Changchun, 130012, Jilin, China
| | - Yan Liang
- Infrastructure Logistics Office, Jilin Engineering Normal University, Changchun, 130012, Jilin, China.
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Hao Q, Zhang T, Cheng X, He P, Zhu X, Chen Y. GIS-based non-grain cultivated land susceptibility prediction using data mining methods. Sci Rep 2024; 14:4433. [PMID: 38396025 PMCID: PMC10891112 DOI: 10.1038/s41598-024-55002-y] [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: 10/07/2023] [Accepted: 02/19/2024] [Indexed: 02/25/2024] Open
Abstract
The purpose of the present study is to predict and draw up non-grain cultivated land (NCL) susceptibility map based on optimized Extreme Gradient Boosting (XGBoost) model using the Particle Swarm Optimization (PSO) metaheuristic algorithm. In order to, a total of 184 NCL areas were identified based on historical records, and a total of 16 NCL susceptibility conditioning factors (NCLSCFs) were considered, based on both a systematic literature survey and local environmental conditions. The results showed that the XGBoost model optimized by PSO performed well in comparison to other machine learning algorithms; the values of sensitivity, specificity, PPV, NPV, and AUC are 0.93, 0.89, 0.88, 0.93, and 0.96, respectively. Slope, rainfall, fault density, distance from fault and drainage density are most important variables. According to the results of this study, the use of meta-innovative algorithms such as PSO can greatly enhance the ability of machine learning models.
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Affiliation(s)
- Qili Hao
- Shangluo Branch, Shaanxi Provincial Land Engineering Construction Group, Xi'an, 710075, China.
| | - Tingyu Zhang
- Shangluo Branch, Shaanxi Provincial Land Engineering Construction Group, Xi'an, 710075, China
| | - Xiaohui Cheng
- Shangluo Branch, Shaanxi Provincial Land Engineering Construction Group, Xi'an, 710075, China
| | - Peng He
- Shangluo Tea Research Institute, Shangluo, 726300, China
| | - Xiankui Zhu
- Shangluo Tea Research Institute, Shangluo, 726300, China
| | - Yao Chen
- Shangnan County Tea Industry Development Center, Shangluo, 726300, China
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Tavakol Aghaei V, SeyyedAbbasi A, Rasheed J, Abu-Mahfouz AM. Sand cat swarm optimization-based feedback controller design for nonlinear systems. Heliyon 2023; 9:e13885. [PMID: 36895404 PMCID: PMC9989652 DOI: 10.1016/j.heliyon.2023.e13885] [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: 12/14/2022] [Revised: 02/14/2023] [Accepted: 02/15/2023] [Indexed: 03/03/2023] Open
Abstract
The control of the open loop unstable systems with nonlinear structure is challenging work. In this paper, for the first time, we present a sand cat swarm optimization (SCSO) algorithm-based state feedback controller design for open-loop unstable systems. The SCSO algorithm is a newly proposed metaheuristic algorithm with an easy-to-implement structure that can efficiently find the optimal solution for optimization problems. The proposed SCSO-based state feedback controller can successfully optimize the control parameters with efficient convergence curve speed. In order to show the performance of the proposed method, three different nonlinear control systems such as an Inverted pendulum, a Furuta pendulum, and an Acrobat robot arm are considered. The control and optimization performances of the proposed SCSO algorithm are compared with well-known metaheuristic algorithms. The simulation results show that the proposed control method can either outperform the compared metaheuristic-based algorithms or have competitive results.
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Affiliation(s)
- Vahid Tavakol Aghaei
- Istinye University, Faculty of Engineering and Natural Sciences, Electrical and Electronics Engineering, Istanbul, Turkiye
| | - Amir SeyyedAbbasi
- Istinye University, Faculty of Engineering and Natural Sciences, Software Engineering, Istanbul, Turkiye
| | - Jawad Rasheed
- Department of Software Engineering, Istanbul Nişantaşı University, 34398 Istanbul, Turkiye
| | - Adnan M Abu-Mahfouz
- Council for Scientific and Industrial Research (CSIR), Pretoria 0184, South Africa.,Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa
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Cinar AC. A review of 10 × 10 and 20 × 20 grid-type wind turbine placement problems solving by metaheuristics. Environ Sci Pollut Res Int 2023; 30:11359-11377. [PMID: 36527561 DOI: 10.1007/s11356-022-24738-3] [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] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 12/08/2022] [Indexed: 06/17/2023]
Abstract
Wind energy is the most important renewable energy source produced by wind turbines. The optimal placement of wind turbines is a challenging binary optimization problem. Maximizing production capacity and minimizing the number of turbines (accordingly minimizing the installation cost) are the main objectives. Metaheuristic algorithms can solve binary optimization problems with optimal or near-optimal solutions. In the literature, the wind turbine placement problem (WTPP) is solved by metaheuristic algorithms from 1994 to 2020. In this work, a literature review of solving 10 × 10 and 20 × 20 grid-type WTPPs with metaheuristic algorithms is conducted. Forty-six different papers were deeply discussed and presented all the experimental results to shed light on future studies for presenting more robust metaheuristics for solving WTPPs. The key results of this review are precaution against false comparisons; presenting the current experimental results; determining the comparison parameters; and demonstrating the benchmark problem clearly. Future directions were clearly spotlighted for practitioners and researchers and new research topics for potential studies are also presented.
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Affiliation(s)
- Ahmet Cevahir Cinar
- Department of Computer Engineering, Faculty of Technology, Selçuk University, Konya, Turkey.
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Mehmood N, Umer M, Asgher U. Application of hybrid SFLA-ACO algorithm and CAM softwares for optimization of drilling tool path problems. SN Appl Sci 2023; 5:61. [PMID: 36712556 DOI: 10.1007/s42452-022-05271-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 12/22/2022] [Indexed: 01/22/2023] Open
Abstract
Abstract In drilling process almost seventy percent time is spent in tool switching and moving the spindle from one hole to the other. This time travel is non productive as it does not take part in actual drilling process. Therefore, this non productive time needs to be optimized. Different metaheuristic algorithms have been applied to minimize this non productive tool travel time. In this study, two metaheuristic approaches, shuffled frog leaping algorithm (SFLA) and ant colony optimization (ACO) have been hybridized. In industry, the CAM softwares are employed for minimization of non productive tool travel time and it is considered that the path obtained by using the CAM softwares is the optimized path. However this is not the case in all problems. In order to show the contribution of the SFLA-ACO algorithm and to prove that results achieved through CAM softwares are not always optimized, hybrid SFLA-ACO algorithm has been applied to two drilling problems as case studies with the main objective of minimization of non productive tool travel time. The drilling problems which are taken from the manufacturing industry include ventilator manifold problem and lift axle mounting bracket problem. The results of hybrid SFLA-ACO algorithm have been compared with the results of commercially available computer aided manufacturing (CAM) software. For comparison purpose, the CAM softwares used are Creo 6.0, Pro E, Siemens NX and Solidworks. The comparison shows that the results of proposed hybrid SFLA-ACO algorithm are better than commercially available CAM softwares in both real world manufacturing problems. Article highlights Different optimization techniques are being used for optimization of drilling tool path problems. In this paper two techniques SFLA and ACO has been combined to form a hybrid SFLA-ACO algorithm and has been applied to the real world industrial problems.Two real world problems have been taken from the local manufacturing industries. In both the problems the objective is to optimize the tool traveling time through hybrid SFLA-ACO and compare it with CAM software.Four CAM softwares have been used for comparison purpose. The problems undertaken are solved through these CAM software and compared with the results of hybrid SFLA-ACO results. As result of comparison it is found that in both the problems the performance of hybrid SFLA-ACO algorithm remains outclass. This signifies that results of CAM software in case of optimization of drilling tool path are not always optimal and these can be improved by using different optimization techniques.
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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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Foroughi A, Moghaddam BF, Behzadi MH, Sobhani FM. Developing a bi-objective resilience relief logistic considering operational and disruption risks: a post-earthquake case study in Iran. Environ Sci Pollut Res Int 2022; 29:56323-56340. [PMID: 35332457 DOI: 10.1007/s11356-022-18699-w] [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] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 01/12/2022] [Indexed: 06/14/2023]
Abstract
Today, according to the occurrence of numerous disasters in allover over the world, designing the proper and comprehensive plan for relief logistics has received a lot of attention from crisis managers and people. Besides, considering resilience capability along with operational and disruption risks leads to the robustness of the humanitarian relief chain (HRC), and this comprehensive framework ensures the essential supplies delivery to the beneficiaries and is close to real-world problems. The resilience parameters used for the second objective are obtained by a strong Best Worst Method (BWM). Another supposition of the model is the consideration of uncertainty in all stages of the proposed problem. Moreover, the multiple disasters (sub-sequent minor post disasters) which can increase the initial demand are considered. Furthermore, the proposed model is solved using three well-known metaheuristic algorithms includes non-dominated sorting genetic algorithm (NSGA-II), network reconfiguration genetic algorithm (NRGA), and multi-objective particle swarm optimization (MOPSO), and their performance is compared by several standard multi-objective measure metrics. Finally, the obtained results show the robustness of the proposed approaches, and some directions for future researches are provided.
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Affiliation(s)
- Amin Foroughi
- Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | | | - Mohammad Hassan Behzadi
- Department of Statistics, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Farzad Movahedi Sobhani
- Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
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9
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Bonah E, Huang X, Hongying Y, Aheto JH, Yi R, Yu S, Tu H. Detection of Salmonella Typhimurium contamination levels in fresh pork samples using electronic nose smellprints in tandem with support vector machine regression and metaheuristic optimization algorithms. J Food Sci Technol 2021; 58:3861-3870. [PMID: 34471310 PMCID: PMC8357911 DOI: 10.1007/s13197-020-04847-y] [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] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Revised: 08/29/2020] [Accepted: 10/08/2020] [Indexed: 11/28/2022]
Abstract
Rapid detection and quantification of bacterial foodborne pathogens are crucial in reducing the incidence of diseases associated with meat products contaminated with pathogens. For the identification, discrimination and quantification of Salmonella Typhimurium contamination in pork samples, a commercial electronic nose with ten (10) metal oxide semiconductor sensor array is applied. Principal component analysis was successfully applied for discrimination of inoculated samples and inoculated samples at different contaminant levels. Support vector machine regression (SVMR) together with a metaheuristic framework using genetic algorithm (GA), particle swarm optimization (PSO), and grid searching (GS) optimization algorithms were applied for S. Typhimurium quantification. Although SVMR results were satisfactory, SVMR hyperparameter tuning (c and g) by PSO, GA and GS showed superior performance of the models. The order of the prediction accuracy based on the prediction set was GA-SVMR (R P 2 = 0.989; RMSEP = 0.137; RPD = 14.93) > PSO-SVMR (R P 2 = 0.986; RMSEP = 0.145; RPD = 14.11) > GS-SVMR (R P 2 = 0.966; RMSEP = 0.148; RPD = 13.82) > SVMR (R P 2 = 0.949; RMSEP = 0.162; RPD = 12.63). GA-SVMR's proposed approach was fairly more effective and retained an excellent prediction accuracy. A clear relationship was identified between odor analysis results, and reference traditional microbial test, indicating that the electronic nose is useful for accurate microbial volatile organic compound evaluation in the quantification of S. Typhimurium in a food matrix.
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Affiliation(s)
- Ernest Bonah
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013 Jiangsu People’s Republic of China
- Laboratory Services Department, Food and Drugs Authority, P. O. Box CT 2783, Cantonments, Accra, Ghana
| | - Xingyi Huang
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013 Jiangsu People’s Republic of China
| | - Yang Hongying
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013 Jiangsu People’s Republic of China
| | - Joshua Harrington Aheto
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013 Jiangsu People’s Republic of China
| | - Ren Yi
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013 Jiangsu People’s Republic of China
- School of Smart Agriculture, Suzhou Polytechnic Institute of Agriculture, XiYuan Road 279, Suzhou, 215000 People’s Republic of China
| | - Shanshan Yu
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013 Jiangsu People’s Republic of China
| | - Hongyang Tu
- School of Food and Biological Engineering, Jiangsu University, Xuefu Road 301, Zhenjiang, 212013 Jiangsu People’s Republic of China
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Wang M, Huang T, Wong DC, Ho KF, Dong G, Yim SHL. A new approach for health-oriented ozone control strategy: Adjoint-based optimization of NO x emission reductions using metaheuristic algorithms. J Clean Prod 2021; 312:127533. [PMID: 34248301 PMCID: PMC8262626 DOI: 10.1016/j.jclepro.2021.127533] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
While levels of particulate matters in the Pearl River Delta Region (PRD) show a significant reduction, ozone (O3) has an opposite increasing trend, becoming the critical air quality target in this decade. Emission control strategies are typically formulated sector by sector, spatial variability in emissions reductions and health impacts of air pollutants may not be taken into account, affecting the overall effectiveness of control strategies. This study proposes an adjoint-based optimization framework to facilitate health-oriented O3 control over PRD. The location-specific adjoint sensitivity coefficients, which reflect the spatiotemporal influences from emissions of nitrogen dioxide (NOx) on O3 health impacts, are combined with metaheuristic algorithms to minimize the O3-related premature mortalities over receptor regions. Using the proposed optimization methodology, the regional O3 health benefits under current emission reduction policy can be increased by 16-27%. The results show that relatively larger NOx emissions reductions occurred at highly developed and populated areas. Particularly, significant reductions in NOx emissions are observed at Shenzhen and urban Guangzhou. Furthermore, implementing regional NOx emissions abatement has advantages to achieve an overall O3 health benefits for all cities. The interregional influences of NOx emissions abatement between cities indicate a promising strategy of health-oriented O3 control in PRD.
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Affiliation(s)
- Mengya Wang
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Sha Tin, N.T., Hong Kong, China
| | - Tao Huang
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Sha Tin, N.T., Hong Kong, China
| | - David C. Wong
- Computational Exposure Division, National Exposure Research Laboratory, US Environmental Protection Agency, Hong Kong, China
| | - Kin Fai Ho
- The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China
- Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Sha Tin, N.T., Hong Kong, China
| | - Guanghui Dong
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China
| | - Steve H. L. Yim
- Department of Geography and Resource Management, The Chinese University of Hong Kong, Sha Tin, N.T., Hong Kong, China
- Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Sha Tin, N.T., Hong Kong, China
- Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China
- Asian School of the Environment, Nanyang Technological University, Singapore
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Kavitha T, Mathai PP, Karthikeyan C, Ashok M, Kohar R, Avanija J, Neelakandan S. Deep Learning Based Capsule Neural Network Model for Breast Cancer Diagnosis Using Mammogram Images. Interdiscip Sci 2021; 14:113-129. [PMID: 34338956 DOI: 10.1007/s12539-021-00467-y] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [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: 04/23/2021] [Revised: 07/14/2021] [Accepted: 07/23/2021] [Indexed: 02/07/2023]
Abstract
Breast cancer is a commonly occurring disease in women all over the world. Mammogram is an efficient technique used for screening and identification of abnormalities over the breast region. Earlier identification of breast cancer enhances the prognosis of patients and is mainly based on the experience of the radiologist in interpretation of mammogram with quality of image. The advent of Deep Learning (DL) and Computer Vision techniques is widely used to perform breast cancer diagnosis. This paper presents a new Optimal Multi-Level Thresholding-based Segmentation with DL enabled Capsule Network (OMLTS-DLCN) breast cancer diagnosis model utilizing digital mammograms. The OMLTS-DLCN model involves an Adaptive Fuzzy based median filtering (AFF) technique as a pre-processing step to eradicate the noise that exists in the mammogram images. Besides, Optimal Kapur's based Multilevel Thresholding with Shell Game Optimization (SGO) algorithm (OKMT-SGO) is applied for breast cancer segmentation. In addition, the proposed model involves a CapsNet based feature extractor and Back-Propagation Neural Network (BPNN) classification model is employed to detect the existence of breast cancer. The diagnostic outcomes of the presented OMLTS-DLCN technique is examined by means of benchmark Mini-MIAS dataset and DDSM dataset. The experimental values obtained highlights the superior performance of the OMLTS-DLCN model with a higher accuracy of 98.50 and 97.55% on the Mini-MIAS dataset and DDSM dataset, respectively.
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Affiliation(s)
- T Kavitha
- Department of Computer Applications, Kongu Engineering College, Perundurai, Erode, India
| | - Paul P Mathai
- Department of CSE, Federal Institute of Science and Technology (FISAT), Angamaly, Ernakulam, Kerala, India
| | - C Karthikeyan
- Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
| | - M Ashok
- Department of CSE, Rajalakshmi Institute of Technology, Chennai, India
| | - Rachna Kohar
- School of CSE, Lovely Professional University, Punjab, 144411, India
| | - J Avanija
- Department of CSE, Sree Vidyanikethan Engineering College, Tirupati, India
| | - S Neelakandan
- Department of IT, Jeppiaar Institute of Technology, Sriperumbudur, India.
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12
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Guilbault R, Lalonde S. Tip relief designed to optimize contact fatigue life of spur gears using adapted PSO and Firefly algorithms. SN Appl Sci 2021; 3:66. [PMID: 33490874 PMCID: PMC7801362 DOI: 10.1007/s42452-020-04129-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 12/30/2020] [Indexed: 11/29/2022] Open
Abstract
This paper examines the dynamic performances of circular profile modifications designed to optimize the contact fatigue life of spur gears. It combines the PSO and Firefly metaheuristics to a gear dynamic/degradation model. The objectives are to analyse the ability of optimal corrections to reduce dynamic loads and dynamic transmission error (DTE), and to describe the influence of the modification variables. To reduce computation efforts, the study modifies the original metaheuristics. In the proposed adaptation of the Firefly algorithm, the particle movement hinges on the brightest firefly perceived through the light-absorbing medium. This change reduces the number of function evaluations per iteration. The analysis shows that while the correction length is more influential, both modification amount and length alter the gear behavior, whereas the curvature radius influence remains modest. Curved corrections are more effective in ameliorating contact fatigue life, whereas larger curvature radii are better at reducing the DTE. Compared to the original gear set, the PSO and Firefly versions showed that optimized modifications engender substantial enhancements of the fatigue resistance. Moreover, optimal profiles also reduce both DTE and dynamic factors, but the inverse cannot be assumed.
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Affiliation(s)
- Raynald Guilbault
- Department of Mechanical Engineering, École de technologie supérieure, 1100 Notre-Dame Street West, Montreal, QC H3C 1K3 Canada
| | - Sébastien Lalonde
- Department of Mechanical Engineering, École de technologie supérieure, 1100 Notre-Dame Street West, Montreal, QC H3C 1K3 Canada
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Panahi M, Gayen A, Pourghasemi HR, Rezaie F, Lee S. Spatial prediction of landslide susceptibility using hybrid support vector regression (SVR) and the adaptive neuro-fuzzy inference system (ANFIS) with various metaheuristic algorithms. Sci Total Environ 2020; 741:139937. [PMID: 32574917 DOI: 10.1016/j.scitotenv.2020.139937] [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] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 05/15/2020] [Accepted: 06/01/2020] [Indexed: 06/11/2023]
Abstract
Landslides are natural and sometimes quasi-natural hazards that are destructive to natural resources and cause loss of human life every year. Hence, preparing susceptibility maps for landslide monitoring is essential to minimizing their negative effects. The main aim of the current research was to develop landslide susceptibility maps for Icheon Township, South Korea, using hybrid Machin learning and metaheuristic algorithms, that is, the bee algorithm (Bee), the adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and the grey wolf optimizer (GWO), and to compare their predictive accuracy. Based on identified landslide locations, an inventory map was prepared and divided into training and validation data sets (70%/30%). the predicated model outcomes were validated with root mean square error (RMSE), and area under receiver operating characteristic curve (AUC), and pairwise comparison values for the ANFIS, ANFIS-Bee, ANFIS-GWO, SVR, SVR-Bee, and SVR-GWO models were obtained. The area under the curve was obtained with the training and validation data sets. Based on the training data sets, AUC of 80%, 83%, 83%, 69%, 81%, and 80% were obtained for the SVR, SVR-GWO, SVR-Bee, ANFIS, ANFIS-GWO, and ANFIS-Bee models, respectively. For the validation data sets, values of 79%, 82%, 82%, 68%, 79%, and 79%, respectively, were obtained. The SVR-GWO and SVR-Bee models were the most predictive models in terms of constructing the exceptionally focused landslide susceptibility map, with little spatial variation in the highly susceptible classes. Furthermore, the MSE, RMSE, and pairwise comparisons indicated that the SVR-GWO and SVR-Bee models were superior models for this study township. In addition, ANFIS individually was not superior to the ensembles of ANFIS-GWO and ANFIS-Bee for landslide assessment. These landslide susceptibility maps provide a platform for land use planning with an eye toward sustainable development of infrastructure and damage reduction for Icheon Township.
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Affiliation(s)
- Mahdi Panahi
- Division of Science Education, Kangwon National University, Chuncheon-si, Gangwon-do 24341, Republic of Korea; Geoscience Platform Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, Republic of Korea
| | - Amiya Gayen
- Department of Geography, University of Calcutta, Kolkata, India
| | - Hamid Reza Pourghasemi
- Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran.
| | - Fatemeh Rezaie
- Geoscience Platform Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, Republic of Korea; Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 34113, Republic of Korea
| | - Saro Lee
- Geoscience Platform Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, Republic of Korea; Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 34113, Republic of Korea.
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14
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Bukar AL, Tan CW, Yiew LK, Ayop R, Tan WS. A rule-based energy management scheme for long-term optimal capacity planning of grid-independent microgrid optimized by multi-objective grasshopper optimization algorithm. Energy Convers Manag 2020; 221:113161. [PMID: 32834297 PMCID: PMC7372290 DOI: 10.1016/j.enconman.2020.113161] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/26/2020] [Accepted: 06/27/2020] [Indexed: 05/31/2023]
Abstract
Off-grid electrification of remote communities using sustainable energy systems (SESs) is a requisite for realizing sustainable development goals. Nonetheless, the capacity planning of the SESs is challenging as it needs to fulfil the fluctuating demand from a long-term perspective, in addition to the intermittency and unpredictable nature of renewable energy sources (RESs). Owing to the nonlinear and non-convex nature of the capacity planning problem, an efficient technique must be employed to achieve a cost-effective system. Existing techniques are, subject to some constraints on the derivability and continuity of the objective function, prone to premature convergence, computationally demanding, follows rigorous procedures to fine-tune the algorithm parameters in different applications, and often do not offer a fair balance during the exploitation and exploration phase of the optimization process. Furthermore, the literature review indicates that researchers often do not implement and examine the energy management scheme (EMS) of a microgrid while computing for the capacity planning problem of microgrids. This paper proposes a rule-based EMS (REMS) optimized by a nature-inspired grasshopper optimization algorithm (GOA) for long-term capacity planning of a grid-independent microgrid incorporating a wind turbine, a photovoltaic, a battery (BT) bank and a diesel generator ( D g e n ). In which, a rule-based algorithm is used to implement an EMS to prioritize the usage of RES and coordinate the power flow of the proposed microgrid components. Subsequently, an attempt is made to explore and confirm the efficiency of the proposed REMS incorporated with GOA. The ultimate goal of the objective function is to minimize the cost of energy (COE) and the deficiency of power supply probability (DPSP). The performance of the REMS is examined via a long-term simulation study to ascertain the REMS resiliency and to ensure the operating limit of the BT storage is not violated. The result of the GOA is compared with particle swarm optimization (PSO) and a cuckoo search algorithm (CSA). The simulation results indicate that the proposed technique's superiority is confirmed in terms of convergence to the optimal solution. The simulation results confirm that the proposed REMS has contributed to better adoption of a cleaner energy production system, as the scheme significantly reduces fuel consumption, CO 2 emission and COE by 92.4%, 92.3% and 79.8%, respectively as compared to the conventional D g e n . The comparative evaluation of the algorithms shows that REMS-GOA yields a better result as it offers the least COE (objective function), at $0.3656/kW h, as compared to the REMS-CSA at $0.3662/kW h and REMS-PSO at $0.3674/kW h, for the desired DPSP of 0%. Finally, sensitivity analysis is performed to highlight the effect of uncertainties on the system inputs that may arise in the future.
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Affiliation(s)
- Abba Lawan Bukar
- Division of Electrical Power Engineering, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, Malaysia
- Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Maiduguri, P.M.B. 1069, Maiduguri, Borno State, Nigeria
| | - Chee Wei Tan
- Division of Electrical Power Engineering, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, Malaysia
| | - Lau Kwan Yiew
- Institute of High Voltage and High Current, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310 Johor, Malaysia
| | - Razman Ayop
- Division of Electrical Power Engineering, School of Electrical Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), 81310 Skudai, Johor, Malaysia
| | - Wen-Shan Tan
- School of Engineering and Advanced Engineering Platform, Monash University, Jalan Lagoon 47500 Bandar Sunway, Selangor, Malaysia
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15
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Tikhamarine Y, Malik A, Souag-Gamane D, Kisi O. Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration. Environ Sci Pollut Res Int 2020; 27:30001-30019. [PMID: 32445152 DOI: 10.1007/s11356-020-08792-3] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [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: 11/25/2019] [Accepted: 04/06/2020] [Indexed: 06/11/2023]
Abstract
Accurate estimation of reference evapotranspiration (ETo) is profoundly crucial in crop modeling, sustainable management, hydrological water simulation, and irrigation scheduling, since it accounts for more than two-thirds of global precipitation losses. Therefore, ETo-based estimation is a major concern in the hydrological cycle. The estimation of ETo can be determined using various methods, including field measurement (the scale of the lysimeter), experimental methods, and mathematical equations. The Food and Agriculture Organization recommended the Penman-Monteith (FAO-56 PM) method which was identified as the standard method of ETo estimation. However, this equation requires a large number of measured climatic data (maximum and minimum air temperature, relative humidity, solar radiation, and wind speed) that are not always available on meteorological stations. Over the decade, the artificial intelligence (AI) models have received more attention for estimating ETo on multi-time scales. This research explores the potential of new hybrid AI model, i.e., support vector regression (SVR) integrated with grey wolf optimizer (SVR-GWO) for estimating monthly ETo at Algiers, Tlemcen, and Annaba stations located in the north of Algeria. Five climatic variables namely relative humidity (RH), maximum and minimum air temperatures (Tmax and Tmin), solar radiation (Rs), and wind speed (Us) were used for model construction and evaluation. The proposed hybrid SVR-GWO model was compared against hybrid SVR-genetic algorithm (SVR-GA), SVR-particle swarm optimizer (SVR-PSO), conventional artificial neural network (ANN), and empirical (Turc, Ritchie, Thornthwaite, and three versions of Valiantzas methods) models by using root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC), and Willmott index (WI), and through graphical interpretation. Through the results obtained, the performance of the SVR-GWO provides very promising and occasionally competitive results compared to other data-driven and empirical methods at study stations. Thus, the proposed SVR-GWO model with five climatic input variables outperformed the other models (RMSE = 0.0776/0.0613/0.0374 mm, NSE = 0.9953/ 0.9990/0.9995, PCC = 0.9978/0.9995/0.9998 and WI = 0.9988/0.9997/0.9999) for estimating ETo at Algiers, Tlemcen, and Annaba stations, respectively. In conclusion, the results of this research indicate the suitability of the proposed hybrid artificial intelligence model (SVR-GWO) at the study stations. Besides, promising results encourage researchers to transfer and test these models in other locations in the world in future works.
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Affiliation(s)
- Yazid Tikhamarine
- Leghyd Laboratory, Department of Civil Engineering, University of Sciences and Technology Houari Boumediene, BP 32 Al Alia, Babezzouar, Algiers, Algeria
| | - Anurag Malik
- Department of Soil and Water Conservation Engineering, College of Technology, G.B. Pant University of Agriculture and Technology, Pantnagar, Uttarakhand, 263145, India.
| | - Doudja Souag-Gamane
- Leghyd Laboratory, Department of Civil Engineering, University of Sciences and Technology Houari Boumediene, BP 32 Al Alia, Babezzouar, Algiers, Algeria
| | - Ozgur Kisi
- School of Technology, Ilia State University, Tbilisi, Georgia
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16
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Lee MK, Mohamad MS, Choon YW, Mohd Daud K, Nasarudin NA, Ismail MA, Ibrahim Z, Napis S, Sinnott RO. Comparison of Optimization-Modelling Methods for Metabolites Production in Escherichia coli. J Integr Bioinform 2020; 17:jib-2019-0073. [PMID: 32374287 PMCID: PMC7734505 DOI: 10.1515/jib-2019-0073] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [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: 08/30/2019] [Accepted: 01/18/2020] [Indexed: 11/15/2022] Open
Abstract
The metabolic network is the reconstruction of the metabolic pathway of an organism that is used to represent the interaction between enzymes and metabolites in genome level. Meanwhile, metabolic engineering is a process that modifies the metabolic network of a cell to increase the production of metabolites. However, the metabolic networks are too complex that cause problem in identifying near-optimal knockout genes/reactions for maximizing the metabolite’s production. Therefore, through constraint-based modelling, various metaheuristic algorithms have been improvised to optimize the desired phenotypes. In this paper, PSOMOMA was compared with CSMOMA and ABCMOMA for maximizing the production of succinic acid in E. coli. Furthermore, the results obtained from PSOMOMA were validated with results from the wet lab experiment.
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Affiliation(s)
- Mee K Lee
- Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Skudai Johor, Malaysia
| | - Mohd Saberi Mohamad
- Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100 Kota Bharu Kelantan, Malaysia.,Faculty of Bioengineering and Technology, Universiti Malaysia Kelantan, Jeli Campus, Lock Bag 100, 17600 Jeli Kelantan, Malaysia
| | - Yee Wen Choon
- Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Skudai Johor, Malaysia
| | - Kauthar Mohd Daud
- Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Skudai Johor, Malaysia
| | - Nurul Athirah Nasarudin
- Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Skudai Johor, Malaysia
| | - Mohd Arfian Ismail
- Soft Computing and Intelligent System Research Group, Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, 26300 Kuantan, Pahang, Malaysia
| | - Zuwairie Ibrahim
- Faculty of Manufacturing Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Suhaimi Napis
- Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
| | - Richard O Sinnott
- School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC 3052 Australia
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17
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Shekhawat S, Saxena A. Development and applications of an intelligent crow search algorithm based on opposition based learning. ISA Trans 2020; 99:210-230. [PMID: 31515097 DOI: 10.1016/j.isatra.2019.09.004] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 08/30/2019] [Accepted: 09/01/2019] [Indexed: 06/10/2023]
Abstract
Metaheuristics are proven beneficial tools for solving complex, hard optimization problems. Recently, a plethora of work has been reported on bio inspired optimization algorithms. These algorithms are mimicry of behavior of animals, plants and processes into mathematical paradigms. With these developments, a new entrant in this group is Crow Search Algorithm (CSA). CSA is based on the strategic behavior of crows while searching food, thievery and chasing behavior. This algorithm sometimes suffers with local minima stagnation and unbalance exploration and exploitation phases. To overcome this problem, a cosine function is proposed first, to accelerate the exploration and retard the exploitation process with due course of the iterative process. Secondly the opposition based learning concept is incorporated for enhancing the exploration virtue of CSA. The evolved variant with the inculcation of these two concepts is named as Intelligent Crow Search Algorithm (ICSA). The algorithm is benchmarked on two benchmark function sets, one is the set of 23 standard test functions and another is set of latest benchmark function CEC-2017. Further, the applicability of this variant is tested over structural design problem, frequency wave synthesis problem and Model Order Reduction (MOR). Results reveal that ICSA exhibits competitive performance on benchmarks and real applications when compared with some contemporary optimizers.
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Affiliation(s)
- Shalini Shekhawat
- Department of Mathematics, Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur, 302017, India.
| | - Akash Saxena
- Department of Electrical Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur, 302017, India.
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