{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T04:09:27Z","timestamp":1774066167262,"version":"3.50.1"},"reference-count":94,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T00:00:00Z","timestamp":1638921600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shaanxi Provincial Key Research and Development Project","award":["No. 2019GY-099"],"award-info":[{"award-number":["No. 2019GY-099"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 61871318"],"award-info":[{"award-number":["No. 61871318"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open project of Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing","award":["No. 2020CP10"],"award-info":[{"award-number":["No. 2020CP10"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The probability of the basic HHO algorithm in choosing different search methods is symmetric: about 0.5 in the interval from 0 to 1. The optimal solution from the previous iteration of the algorithm affects the current solution, the search for prey in a linear way led to a single search result, and the overall number of updates of the optimal position was low. These factors limit Harris Hawks optimization algorithm. For example, an ease of falling into a local optimum and the efficiency of convergence is low. Inspired by the prey hunting behavior of Harris\u2019s hawk, a multi-strategy search Harris Hawks optimization algorithm is proposed, and the least squares support vector machine (LSSVM) optimized by the proposed algorithm was used to model the reactive power output of the synchronous condenser. Firstly, we select the best Gauss chaotic mapping method from seven commonly used chaotic mapping population initialization methods to improve the accuracy. Secondly, the optimal neighborhood perturbation mechanism is introduced to avoid premature maturity of the algorithm. Simultaneously, the adaptive weight and variable spiral search strategy are designed to simulate the prey hunting behavior of Harris hawk to improve the convergence speed of the improved algorithm and enhance the global search ability of the improved algorithm. A numerical experiment is tested with the classical 23 test functions and the CEC2017 test function set. The results show that the proposed algorithm outperforms the Harris Hawks optimization algorithm and other intelligent optimization algorithms in terms of convergence speed, solution accuracy and robustness, and the model of synchronous condenser reactive power output established by the improved algorithm optimized LSSVM has good accuracy and generalization ability.<\/jats:p>","DOI":"10.3390\/sym13122364","type":"journal-article","created":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T23:30:00Z","timestamp":1639006200000},"page":"2364","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Harris Hawks Optimization with Multi-Strategy Search and Application"],"prefix":"10.3390","volume":"13","author":[{"given":"Shangbin","family":"Jiao","sequence":"first","affiliation":[{"name":"School of Automation and Information Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"},{"name":"Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Chen","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation and Information Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Rui","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Automation and Information Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"},{"name":"Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi\u2019an University of Technology, Xi\u2019an 710048, China"},{"name":"School of Electronic & Electrical Engineering, Baoji University of Arts and Sciences, Baoji 721016, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5035-223X","authenticated-orcid":false,"given":"Yuxing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation and Information Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Qing","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation and Information Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,8]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"A Hybrid ITLHHO Algorithm for Numerical and Engineering Optimization Problems","volume":"36","author":"Kundu","year":"2021","journal-title":"Int. J. Intell. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/j.ins.2018.11.041","article-title":"A Hybrid GSA-GA Algorithm for Constrained Optimization Problems","volume":"478","author":"Garg","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_3","first-page":"292","article-title":"A hybrid PSO-GA algorithm for constrained optimization problems","volume":"274","author":"Garg","year":"2016","journal-title":"Appl. Math. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"100844","DOI":"10.1016\/j.swevo.2021.100844","article-title":"A Survey on Population-Based Meta-Heuristic Algorithms for Motion Planning of Aircraft","volume":"62","author":"Wu","year":"2021","journal-title":"Swarm Evol. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8939","DOI":"10.1007\/s00521-021-05720-5","article-title":"Harris hawks optimization: A comprehensive review of recent variants and applications","volume":"33","author":"Alabool","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Vasant, P. (2015). Handbook of Research on Artificial Intelligence Techniques and Algorithms, IGI Global.","DOI":"10.4018\/978-1-4666-7258-1"},{"key":"ref_7","unstructured":"Simon, D. (2013). Evolutionary Optimization Algorithms: Biologically-Inspired and Population-Based Approaches to Computer Intelligence, John Wiley & Sons Inc."},{"key":"ref_8","unstructured":"Dr\u00e9o, J. (2006). Metaheuristics for Hard Optimization: Methods and Case Studies, Springer."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physrep.2016.08.001","article-title":"Modern Meta-Heuristics Based on Nonlinear Physics Processes: A Review of Models and Design Procedures","volume":"655","year":"2016","journal-title":"Phys. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107128","DOI":"10.1016\/j.epsr.2021.107128","article-title":"Decentralized Multi-Area Multi-Agent Economic Dispatch Model Using Select Meta-Heuristic Optimization Algorithms","volume":"195","author":"Adeyanju","year":"2021","journal-title":"Electr. Power Syst. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2021\/9705982","article-title":"Compact Sine Cosine Algorithm with Multigroup and Multistrategy for Dispatching System of Public Transit Vehicles","volume":"2021","author":"Zhu","year":"2021","journal-title":"J. Adv. Transp."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.future.2018.08.031","article-title":"WSNs-Assisted Opportunistic Network for Low-Latency Message Forwarding in Sparse Settings","volume":"91","author":"Fu","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106926","DOI":"10.1016\/j.knosys.2021.106926","article-title":"SSC: A Hybrid Nature-Inspired Meta-Heuristic Optimization Algorithm for Engineering Applications","volume":"222","author":"Dhiman","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5530","DOI":"10.1021\/acs.iecr.0c06041","article-title":"Improved Multipopulation Discrete Differential Evolution Algorithm for the Scheduling of Multipurpose Batch Plants","volume":"60","author":"Han","year":"2021","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.ejor.2003.08.029","article-title":"Solving multi-objective production scheduling problems using meta-heuristics","volume":"161","author":"Loukil","year":"2005","journal-title":"Eur. J.Oper. Res."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, Q., Chen, H., Huang, H., Zhao, X., Cai, Z., Tong, C., Liu, W., and Tian, X. (2017). An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis. Comput. Math. Methods Med., 1\u201315.","DOI":"10.1155\/2017\/9512741"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2818","DOI":"10.1109\/TMI.2020.2976825","article-title":"Deep Spatial-Temporal Feature Fusion From Adaptive Dynamic Functional Connectivity for MCI Identification","volume":"39","author":"Li","year":"2020","journal-title":"IEEE Trans. Med. Imaging."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s10479-021-04075-3","article-title":"A Novel Hybrid PSO-Based Metaheuristic for Costly Portfolio Selection Problems","volume":"304","author":"Corazza","year":"2021","journal-title":"Ann. Oper. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.1080\/14697680903460168","article-title":"Hybrid Metaheuristics for Constrained Portfolio Selection Problems","volume":"11","author":"Gaspero","year":"2011","journal-title":"Quant. Financ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.knosys.2016.01.002","article-title":"Evolving Support Vector Machines Using Fruit Fly Optimization for Medical Data Classification","volume":"96","author":"Shen","year":"2016","journal-title":"Knowl.-Based Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.neucom.2017.04.060","article-title":"Toward an Optimal Kernel Extreme Learning Machine Using a Chaotic Moth-Flame Optimization Strategy with Applications in Medical Diagnoses","volume":"267","author":"Wang","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1109\/TIE.2021.3050348","article-title":"Self-Triggered Sliding Mode Control for Networked PMSM Speed Regulation System: A PSO-Optimized Super-Twisting Algorithm","volume":"69","author":"Song","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","article-title":"The whale optimization algorithm","volume":"95","author":"Mirjalili","year":"2016","journal-title":"Adv. Eng. Softw."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","article-title":"Harris hawks optimization: Algorithm and applications","volume":"97","author":"Heidari","year":"2019","journal-title":"Future Gen. Comput. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1135","DOI":"10.1109\/TSMC.2016.2560128","article-title":"A Supervised Learning and Control Method to Improve Particle Swarm Optimization Algorithms","volume":"47","author":"Dong","year":"2017","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1109\/JAS.2017.7510523","article-title":"A Parametric Genetic Algorithm Approach to Assess Complementary Options of Large Scale Windsolar Coupling","volume":"4","author":"Mareda","year":"2017","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1109\/JAS.2018.7511138","article-title":"Modified cuckoo search algorithm to solve economic power dispatch optimization problems","volume":"5","author":"Jian","year":"2018","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1126\/science.220.4598.671","article-title":"Optimization by Simulated Annealing","volume":"220","author":"Kirkpatrick","year":"1983","journal-title":"Science."},{"key":"ref_29","first-page":"355","article-title":"Gravitational Search Algorithm","volume":"Volume 62","author":"Xing","year":"2014","journal-title":"Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"13170","DOI":"10.1016\/j.eswa.2011.04.126","article-title":"ACROA: Artificial chemical reaction optimization algorithm for global optimization","volume":"38","author":"Alatas","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.ins.2015.06.044","article-title":"Heat transfer search (HTS): A novel optimization algorithm","volume":"324","author":"Patel","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2932","DOI":"10.1016\/j.asoc.2012.03.068","article-title":"Gases brownian motion optimization: An algorithm for optimization (GBMO)","volume":"13","author":"Abdechiri","year":"2013","journal-title":"Appl. Soft Comput."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/j.future.2019.07.015","article-title":"Henry gas solubility optimization: A novel physics-based algorithm","volume":"101","author":"Hashim","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","article-title":"Genetic Algorithms","volume":"267","author":"Holland","year":"1992","journal-title":"Sci. Am."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Schneider, B., and Ranft, U. (1978). Simulationsmethoden in der Medizin und Biologie, Springer.","DOI":"10.1007\/978-3-642-81283-5"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/B978-0-12-821986-7.00014-7","article-title":"Differential evolution","volume":"6","author":"Yang","year":"2021","journal-title":"Nature-Inspired Optimization Algorithms. Algorithms"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","article-title":"Differential evolution\u2013A simple and efficient heuristic for global optimization over continuous spaces","volume":"11","author":"Storn","year":"1997","journal-title":"J. Glob. Optim."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"702","DOI":"10.1109\/TEVC.2008.919004","article-title":"Biogeography-Based Optimization","volume":"12","author":"Simon","year":"2008","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_39","first-page":"108","article-title":"A comparative study of Artificial Bee Colony algorithm","volume":"214","author":"Karaboga","year":"2009","journal-title":"Appl. Math. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1504\/IJBIC.2010.032124","article-title":"Firefly algorithm, stochastic test functions and design optimization","volume":"2","author":"Yang","year":"2010","journal-title":"Int. J. Bio-Inspired Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5430\/ijrc.v1n1p1","article-title":"BAS: Beetle antennae search algorithm for optimization problems","volume":"1","author":"Jiang","year":"2018","journal-title":"Int. J. Robot. Control"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Okwu, M.O. (2021). Grey Wolf Optimizer, Metaheuristic Optimization, Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications, Springer International Publishing.","DOI":"10.1007\/978-3-030-61111-8"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.advengsoft.2015.11.004","article-title":"A novel nature-inspired algorithm for optimization: Virus colony search","volume":"92","author":"Li","year":"2016","journal-title":"Adv. Eng. Softw."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1287\/ijoc.1.3.190","article-title":"Tabu search\u2014Part I","volume":"1","author":"Glover","year":"1989","journal-title":"ORSA J. Comput."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.future.2017.10.052","article-title":"Socio evolution & learning optimization algorithm: A socio-inspired optimization methodology","volume":"81","author":"Kumar","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2011.08.006","article-title":"Teaching\u2013learning-based optimization: An optimization method for continuous non-linear large scale problems","volume":"183","author":"Rao","year":"2012","journal-title":"Inf. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1078","DOI":"10.1016\/j.asoc.2014.08.024","article-title":"A Survey on the Imperialist Competitive Algorithm Metaheuristic: Implementation in Engineering Domain and Directions for Future Research","volume":"24","author":"Hosseini","year":"2014","journal-title":"Appl. Soft. Comput."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"28369","DOI":"10.1007\/s11042-020-09228-3","article-title":"Pulse coupled neural network based on Harris hawks optimization algorithm for image segmentation","volume":"79","author":"Jia","year":"2020","journal-title":"Multimed Tools Appl."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1847","DOI":"10.1007\/s12065-020-00465-x","article-title":"Neighborhood centroid opposite-based learning harris hawks optimization for training neural networks","volume":"14","author":"Fan","year":"2020","journal-title":"Evol. Intell."},{"key":"ref_50","first-page":"1902","article-title":"Iot Based Speed Control of BLDC Motor with Harris Hawks Optimization Controller","volume":"13","author":"Saravanan","year":"2020","journal-title":"Int. J. Grid Distrib. Comput."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.apm.2020.03.024","article-title":"Harris hawks optimization with information exchange","volume":"84","author":"Qu","year":"2020","journal-title":"Appl. Math. Model."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Devarapalli, R., and Bhattacharyya, B. (2019, January 20\u201322). Application of modified harris hawks Optimization in power system oscillations damping controller design. Proceedings of the 2019 8th International Conference on Power Systems (ICPS), Jaipur, India.","DOI":"10.1109\/ICPS48983.2019.9067679"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"186638","DOI":"10.1109\/ACCESS.2020.3029728","article-title":"An improved harris hawks optimization algorithm with simulated annealing for feature selection in the Medical Field","volume":"8","author":"Elgamal","year":"2020","journal-title":"IEEE Access."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"106425","DOI":"10.1016\/j.knosys.2020.106425","article-title":"Dimension decided harris hawks optimization with gaussian mutation: Balance analysis and diversity patterns","volume":"215","author":"Song","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/4235.585893","article-title":"No Free Lunch Theorems for Optimization","volume":"1","author":"Wolpert","year":"1997","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1126\/science.239.4847.1525","article-title":"Cooperative Hunting Harris\u2019 Hawks (Parabuteo unicinctus)","volume":"239","author":"Bednarz","year":"1988","journal-title":"Science"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1006\/anbe.1996.0330","article-title":"Feeding innovations and forebrain size in birds","volume":"53","author":"Lefebvre","year":"1997","journal-title":"Anim. Behav."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"5460","DOI":"10.1073\/pnas.0408145102","article-title":"Big brains, Enhanced Cognition, and Response of birds to Novel environments","volume":"102","author":"Sol","year":"2005","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Kazimipour, B., Li, X., and Qin, A.K. (2014, January 6\u201311). A review of population initialization techniques for evolutionary algorithms. Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC), Beijing, China.","DOI":"10.1109\/CEC.2014.6900618"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"12280","DOI":"10.1007\/s11227-021-03737-0","article-title":"Multi-Level Parallel Chaotic Jaya Optimization Algorithms for Solving Constrained Engineering Design Problems","volume":"77","author":"Rico","year":"2021","journal-title":"J. Supercomput."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.knosys.2009.06.004","article-title":"Multi-Objective Rule Mining Using a Chaotic Particle Swarm Optimization Algorithm","volume":"22","author":"Alatas","year":"2009","journal-title":"Knowl.-Based Syst."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"4385","DOI":"10.1007\/s00521-018-3343-2","article-title":"Chaotic Grasshopper Optimization Algorithm for Global Optimization","volume":"31","author":"Arora","year":"2019","journal-title":"Neural Comput. Appl."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.ins.2014.02.123","article-title":"Chaotic Krill Herd Algorithm","volume":"274","author":"Wang","year":"2014","journal-title":"Inf. Sci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.knosys.2015.08.010","article-title":"Chaotic Fruit Fly Optimization Algorithm","volume":"89","year":"2015","journal-title":"Knowl.-Based Syst."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1007\/s10489-018-1301-4","article-title":"A Chaotic Teaching Learning Based Optimization Algorithm for Clustering Problems","volume":"49","author":"Kumar","year":"2019","journal-title":"Appl. Intell."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"106353","DOI":"10.1016\/j.compstruc.2020.106353","article-title":"Chaotic Coyote Algorithm Applied to Truss Optimization Problems","volume":"242","author":"Pierezan","year":"2021","journal-title":"Comput. Struct."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1007\/s10489-018-1261-8","article-title":"Chaotic Dragonfly Algorithm: An Improved Metaheuristic Algorithm for Feature Selection","volume":"49","author":"Sayed","year":"2019","journal-title":"Appl. Intell."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.knosys.2017.10.011","article-title":"Chaotic Dynamic Weight Particle Swarm Optimization for Numerical Function Optimization","volume":"139","author":"Chen","year":"2018","journal-title":"Knowl.-Based Syst."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1007\/s10462-019-09707-6","article-title":"A Novel Chaotic Selfish Herd Optimizer for Global Optimization and Feature Selection","volume":"53","author":"Anand","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/4235.771163","article-title":"Evolutionary Programming Made Faster","volume":"3","author":"Yao","year":"1999","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1080\/00207160108805080","article-title":"On benchmarking functions for genetic algorithms","volume":"77","author":"Digalakis","year":"2001","journal-title":"Int. J. Comput. Math."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.asoc.2019.105884","article-title":"An enhanced Bacterial Foraging Optimization and its application for training kernel extreme learning machine","volume":"86","author":"Chen","year":"2020","journal-title":"Appl. Soft. Comput."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.knosys.2015.12.022","article-title":"SCA: A Sine Cosine Algorithm for Solving Optimization Problems","volume":"96","author":"Mirjalili","year":"2016","journal-title":"Knowl.-Based Syst."},{"key":"ref_74","unstructured":"Tan, Y., Shi, Y., and Coello, C.A.C. (2014). A new bio-inspired algorithm: Chicken swarm optimization. Advances in Swarm Intelligence, Springer International Publishing."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Zhang, J., Chen, J., and Che, L. (2020, January 19\u201324). Hybrid PSO Algorithm with Adaptive Step Search in Noisy and Noise-Free Environments. Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC), Glasgow, UK.","DOI":"10.1109\/CEC48606.2020.9185638"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Jordehi, A.R. (2017, January 5\u20138). Gravitational Search Algorithm with Linearly Decreasing Gravitational Constant for Parameter Estimation of Photovoltaic Cells. Proceedings of the 2017 IEEE Congress on Evolutionary Computation (CEC), Donostia, Spain.","DOI":"10.1109\/CEC.2017.7969293"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Feng, X., Liu, A., Sun, W., Yue, X., and Liu, B. (2018, January 8\u201313). A Dynamic Generalized Opposition-Based Learning Fruit Fly Algorithm for Function Optimization. Proceedings of the 2018 IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, Brazil.","DOI":"10.1109\/CEC.2018.8477794"},{"key":"ref_78","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"Demsar","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"2044","DOI":"10.1016\/j.ins.2009.12.010","article-title":"Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power","volume":"180","author":"Luengo","year":"2010","journal-title":"Inf. Sci."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1711","DOI":"10.1111\/j.1469-8986.2011.01273.x","article-title":"Mass univariate analysis of event-related brain potentials\/fields I: A critical tutorial review","volume":"48","author":"Groppe","year":"2011","journal-title":"Psychophysiology"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s10846-005-9016-2","article-title":"Design and implementation of a fast digital fuzzy logic controller using FPGA Technology","volume":"45","author":"Deliparaschos","year":"2006","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1109\/TPWRS.2009.2030356","article-title":"Dynamic VAr planning in a large power system using trajectory sensitivities","volume":"25","author":"Sapkota","year":"2010","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1109\/TPWRS.2012.2187316","article-title":"Improving performance of Multi-infeed HVDC systems using grid dynamic segmentation technique based on fault current limiters","volume":"27","author":"Huang","year":"2012","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_84","first-page":"47","article-title":"A discussion about standard parameter models of synchronous machine","volume":"12","author":"Yong","year":"2007","journal-title":"Power Syst. Technol."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Grigsby, L.L. (2012). Power system Stability and Control, Taylor & Francis. [3rd ed.].","DOI":"10.1201\/b12113"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"103801","DOI":"10.1016\/j.engappai.2020.103801","article-title":"Backtracking search optimization algorithm-based least square support vector machine and its applications","volume":"94","author":"Tian","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1023\/A:1018628609742","article-title":"Least squares support vector machine classifiers","volume":"9","author":"Suykens","year":"1999","journal-title":"Neural Process. Lett."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.enconman.2016.08.070","article-title":"Implementation of GA-LSSVM modelling approach for estimating the performance of solid desiccant wheels","volume":"127","author":"Zendehboudi","year":"2016","journal-title":"Energy Convers. Manag."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.petrol.2014.10.001","article-title":"Integration of LSSVM technique with PSO to determine asphaltene deposition","volume":"124","author":"Chamkalani","year":"2014","journal-title":"J. Pet. Sci. Eng."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.chaos.2017.03.018","article-title":"A prediction method based on wavelet transform and multiple models fusion for chaotic time series","volume":"98","author":"Zhongda","year":"2017","journal-title":"Chaos Solitons Fractals"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"297","DOI":"10.14257\/ijunesst.2016.9.8.25","article-title":"Research on the supply chain risk assessment based on the improved LSSVM algorithm","volume":"9","author":"Liu","year":"2016","journal-title":"Int. J. U-Serv. Sci. Technol."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.1049\/el.2016.1171","article-title":"Efficient algorithm for classification of electrocardiogram beats based on artificial bee colony-based least-squares support vector machines classifier","volume":"52","author":"Jain","year":"2016","journal-title":"Electron. Lett."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.knosys.2018.08.027","article-title":"Short-term electricity load forecasting based on feature selection and Least Squares Support Vector Machines","volume":"163","author":"Yang","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"ref_94","first-page":"1","article-title":"Support vector machine","volume":"Volume 3","author":"Adankon","year":"2014","journal-title":"Encyclopedia of Biometrics"}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/12\/2364\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:43:17Z","timestamp":1760168597000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/13\/12\/2364"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,8]]},"references-count":94,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["sym13122364"],"URL":"https:\/\/doi.org\/10.3390\/sym13122364","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,8]]}}}