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Zhejiang","award":["2019C01060"],"award-info":[{"award-number":["2019C01060"]}]},{"name":"\u201cPioneer\u201d and \u201cLeading Goose\u201d R&amp;D Program of Zhejiang","award":["2022C01236"],"award-info":[{"award-number":["2022C01236"]}]},{"name":"\u201cPioneer\u201d and \u201cLeading Goose\u201d R&amp;D Program of Zhejiang","award":["174433KYSB20190019"],"award-info":[{"award-number":["174433KYSB20190019"]}]},{"name":"\u201cPioneer\u201d and \u201cLeading Goose\u201d R&amp;D Program of Zhejiang","award":["2019R01003"],"award-info":[{"award-number":["2019R01003"]}]},{"name":"International Partnership Program of Chinese Academy of Sciences","award":["21875271"],"award-info":[{"award-number":["21875271"]}]},{"name":"International Partnership Program of Chinese Academy of Sciences","award":["U20B2021"],"award-info":[{"award-number":["U20B2021"]}]},{"name":"International Partnership Program of Chinese Academy of 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Zhejiang","award":["2019C01060"],"award-info":[{"award-number":["2019C01060"]}]},{"name":"Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang","award":["2022C01236"],"award-info":[{"award-number":["2022C01236"]}]},{"name":"Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang","award":["174433KYSB20190019"],"award-info":[{"award-number":["174433KYSB20190019"]}]},{"name":"Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang","award":["2019R01003"],"award-info":[{"award-number":["2019R01003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The Chimp Optimization Algorithm (ChOA) is a heuristic algorithm proposed in recent years. It models the cooperative hunting behaviour of chimpanzee populations in nature and can be used to solve numerical as well as practical engineering optimization problems. ChOA has the problems of slow convergence speed and easily falling into local optimum. In order to solve these problems, this paper proposes a novel chimp optimization algorithm with refraction learning (RL-ChOA). In RL-ChOA, the Tent chaotic map is used to initialize the population, which improves the population\u2019s diversity and accelerates the algorithm\u2019s convergence speed. Further, a refraction learning strategy based on the physical principle of light refraction is introduced in ChOA, which is essentially an Opposition-Based Learning, helping the population to jump out of the local optimum. Using 23 widely used benchmark test functions and two engineering design optimization problems proved that RL-ChOA has good optimization performance, fast convergence speed, and satisfactory engineering application optimization performance.<\/jats:p>","DOI":"10.3390\/a15060189","type":"journal-article","created":{"date-parts":[[2022,5,31]],"date-time":"2022-05-31T09:24:29Z","timestamp":1653989069000},"page":"189","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications"],"prefix":"10.3390","volume":"15","author":[{"given":"Quan","family":"Zhang","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiyu","family":"Du","sequence":"additional","affiliation":[{"name":"Engineering Laboratory of Advanced Energy Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiming","family":"Zhang","sequence":"additional","affiliation":[{"name":"Engineering Laboratory of Advanced Energy Materials, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongzhuo","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Duan","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanru","family":"Lin","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,31]]},"reference":[{"key":"ref_1","unstructured":"Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., Inc.. [1st ed.]."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Van Laarhoven, P.J.M., and Aarts, E.H.L. (1987). Simulated annealing. Simulated Annealing: Theory and Applications, Springer.","DOI":"10.1007\/978-94-015-7744-1"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"173548","DOI":"10.1109\/ACCESS.2020.3024108","article-title":"Crow search algorithm: Theory, recent advances, and applications","volume":"8","author":"Hussien","year":"2020","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MCI.2006.329691","article-title":"Ant colony optimization","volume":"1","author":"Dorigo","year":"2006","journal-title":"IEEE. Comput. Intell. Mag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","article-title":"Differential Evolution\u2014A Simple and Efficient Heuristic for global Optimization over Continuous Spaces","volume":"11","author":"Storn","year":"1997","journal-title":"J. Glob. Optim."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1942","DOI":"10.1109\/ICNN.1995.488968","article-title":"Particle swarm optimization","volume":"Volume 4","author":"Kennedy","year":"1995","journal-title":"Proceedings of the ICNN\u201995-International Conference on Neural Networks"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yang, X.S. (2010). A New Metaheuristic Bat-Inspired Algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Springer.","DOI":"10.1007\/978-3-642-12538-6_6"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s00366-011-0241-y","article-title":"Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems","volume":"29","author":"Gandomi","year":"2013","journal-title":"Eng. Comput."},{"key":"ref_9","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_10","unstructured":"Watanabe, O., and Zeugmann, T. Firefly Algorithms for Multimodal Optimization. Proceedings of the Stochastic Algorithms: Foundations and Applications, Sapporo, Japan, 26\u201328 October 2009."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","article-title":"Grey Wolf Optimizer","volume":"69","author":"Mirjalili","year":"2014","journal-title":"Adv. Eng. Softw."},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1007\/s10898-007-9149-x","article-title":"A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm","volume":"39","author":"Karaboga","year":"2007","journal-title":"J. Glob. Optim."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"113338","DOI":"10.1016\/j.eswa.2020.113338","article-title":"Chimp optimization algorithm","volume":"149","author":"Khishe","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Masehian, E., and Sedighizadeh, D. (2010, January 14\u201317). A multi-objective PSO-based algorithm for robot path planning. Proceedings of the 2010 IEEE International Conference on Industrial Technology, Vi\u00f1a del Mar, Chile.","DOI":"10.1109\/ICIT.2010.5472755"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wang, J., Li, X., Huang, S., and Wang, X. (2021). Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework. Algorithms, 14.","DOI":"10.3390\/a14110324"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.compbiolchem.2007.09.005","article-title":"Improved binary PSO for feature selection using gene expression data","volume":"32","author":"Chuang","year":"2008","journal-title":"Comput. Biol. Chem."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Almomani, O. (2020). A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms. Symmetry, 12.","DOI":"10.3390\/sym12061046"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/s12293-019-00283-4","article-title":"Mathematical modeling and a discrete artificial bee colony algorithm for the welding shop scheduling problem","volume":"11","author":"Li","year":"2019","journal-title":"Memet. Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1016\/j.energy.2016.05.105","article-title":"Economic dispatch using hybrid grey wolf optimizer","volume":"111","author":"Jayabarathi","year":"2016","journal-title":"Energy"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1109\/TNNLS.2016.2634548","article-title":"Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks","volume":"29","author":"Emary","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yu, J., Liu, G., Xu, J., Zhao, Z., Chen, Z., Yang, M., Wang, X., and Bai, Y. (2022). A Hybrid Multi-Target Path Planning Algorithm for Unmanned Cruise Ship in an Unknown Obstacle Environment. Sensors, 22.","DOI":"10.3390\/s22072429"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Al-Shourbaji, I., Helian, N., Sun, Y., Alshathri, S., and Abd Elaziz, M. (2022). Boosting Ant Colony Optimization with Reptile Search Algorithm for Churn Prediction. Mathematics, 10.","DOI":"10.3390\/math10071031"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.eswa.2017.04.029","article-title":"Multilevel thresholding using grey wolf optimizer for image segmentation","volume":"86","author":"Khairuzzaman","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Papakostas, G.A., Nolan, J.W., and Mitropoulos, A.C. (2020). Nature-Inspired Optimization Algorithms for the 3D Reconstruction of Porous Media. Algorithms, 13.","DOI":"10.3390\/a13030065"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.engappai.2017.05.003","article-title":"Grey wolf optimization evolving kernel extreme learning machine: Application to bankruptcy prediction","volume":"63","author":"Wang","year":"2017","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1109\/TIE.2016.2607698","article-title":"Grey Wolf Optimizer Algorithm-Based Tuning of Fuzzy Control Systems with Reduced Parametric Sensitivity","volume":"64","author":"Precup","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1016\/j.conengprac.2010.03.001","article-title":"Fuzzy control optimized by PSO for vibration suppression of beams","volume":"18","author":"Marinaki","year":"2010","journal-title":"Control. Eng. Pract."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Castillo, O., Melin, P., and Kacprzyk, J. (2013). Design of Fuzzy Control Systems with Different PSO Variants. Recent Advances on Hybrid Intelligent Systems, Springer.","DOI":"10.1007\/978-3-642-33021-6"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s41601-019-0116-6","article-title":"Interconnected multi-machine power system stabilizer design using whale optimization algorithm","volume":"4","author":"Dasu","year":"2019","journal-title":"Prot. Control. Mod. Power Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/TEVC.2007.896686","article-title":"Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems","volume":"12","author":"Venayagamoorthy","year":"2008","journal-title":"IEEE Trans. Evol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1109\/TEVC.2006.880326","article-title":"A Survey of Particle Swarm Optimization Applications in Electric Power Systems","volume":"13","author":"AlRashidi","year":"2009","journal-title":"IEEE Trans. Evol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.swevo.2017.08.002","article-title":"Binary Grey Wolf Optimizer for large scale unit commitment problem","volume":"38","author":"Panwar","year":"2018","journal-title":"Swarm Evol. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1016\/j.rser.2017.03.097","article-title":"Application of differential evolution algorithm in static and dynamic economic or emission dispatch problem: A review","volume":"77","author":"Jebaraj","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Couceiro, M.S., Rocha, R.P., and Ferreira, N.M.F. (2011, January 1\u20135). A novel multi-robot exploration approach based on Particle Swarm Optimization algorithms. Proceedings of the 2011 IEEE International Symposium on Safety, Security, and Rescue Robotics, Kyoto, Japan.","DOI":"10.1109\/SSRR.2011.6106751"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1007\/s12559-018-9588-3","article-title":"A Cognitively Inspired Hybridization of Artificial Bee Colony and Dragonfly Algorithms for Training Multi-layer Perceptrons","volume":"10","author":"Ghanem","year":"2018","journal-title":"Cognit. Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.apenergy.2017.05.029","article-title":"Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm","volume":"200","author":"Oliva","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4285","DOI":"10.1109\/TVT.2020.2973294","article-title":"Whale Optimization Algorithm with Applications to Resource Allocation in Wireless Networks","volume":"69","author":"Pham","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_39","first-page":"7950348","article-title":"Modified grey wolf optimizer for global engineering optimization","volume":"2016","author":"Mittal","year":"2016","journal-title":"Appl. Comput. Intell. Soft Comput."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez, L., Castillo, O., and Soria, J. (2016, January 24\u201329). Grey wolf optimizer with dynamic adaptation of parameters using fuzzy logic. Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, Canada.","DOI":"10.1109\/CEC.2016.7744183"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Luo, Q., Zhang, S., Li, Z., and Zhou, Y. (2016). A Novel Complex-Valued Encoding Grey Wolf Optimization Algorithm. Algorithms, 9.","DOI":"10.3390\/a9010004"},{"key":"ref_42","first-page":"695637","article-title":"Improved artificial bee colony algorithm and its application in LQR controller optimization","volume":"2014","author":"Wang","year":"2014","journal-title":"Math. Probl. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.knosys.2016.05.052","article-title":"An improved artificial bee colony and its application","volume":"107","author":"Shi","year":"2016","journal-title":"Knowl. Based Syst."},{"key":"ref_44","first-page":"243","article-title":"IWOA: An improved whale optimization algorithm for optimization problems","volume":"6","author":"Yazdani","year":"2019","journal-title":"Mostafa Bozorgi"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"114288","DOI":"10.1016\/j.eswa.2020.114288","article-title":"Feature selection using Binary Crow Search Algorithm with time varying flight length","volume":"168","author":"Chaudhuri","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"114812","DOI":"10.1016\/j.eswa.2021.114812","article-title":"An improved bat algorithm hybridized with extremal optimization and Boltzmann selection","volume":"175","author":"Chen","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"7011","DOI":"10.1007\/s00521-020-05474-6","article-title":"CSCF: A chaotic sine cosine firefly algorithm for practical application problems","volume":"33","author":"Hassan","year":"2021","journal-title":"Neural. Comput. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/TEVC.2007.894200","article-title":"Opposition-Based Differential Evolution","volume":"12","author":"Rahnamayan","year":"2008","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.engappai.2013.12.004","article-title":"A review of opposition-based learning from 2005 to 2012","volume":"29","author":"Xu","year":"2014","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"14227","DOI":"10.1007\/s00521-020-04815-9","article-title":"A chaotic optimization method based on logistic-sine map for numerical function optimization","volume":"32","author":"Demir","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"6168","DOI":"10.1109\/ACCESS.2017.2695498","article-title":"L\u00e9vy flight trajectory-based whale optimization algorithm for global optimization","volume":"5","author":"Ling","year":"2017","journal-title":"IEEE Access"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"4699","DOI":"10.1016\/j.ins.2011.03.016","article-title":"Enhancing particle swarm optimization using generalized opposition-based learning","volume":"181","author":"Wang","year":"2011","journal-title":"Inf. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.eswa.2018.06.023","article-title":"Improved grasshopper optimization algorithm using opposition-based learning","volume":"112","author":"Ewees","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"67205","DOI":"10.1109\/ACCESS.2020.2985498","article-title":"A Novel Ant Colony Optimization Algorithm with Levy Flight","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Kuang, F., Jin, Z., Xu, W., and Zhang, S. (2014, January 6\u201311). A novel chaotic artificial bee colony algorithm based on Tent map. Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC), Beijing, China.","DOI":"10.1109\/CEC.2014.6900278"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"3665","DOI":"10.1109\/JSTARS.2017.2699200","article-title":"A Novel Adaptive Cuckoo Search Algorithm for Contrast Enhancement of Satellite Images","volume":"10","author":"Suresh","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Afrabandpey, H., Ghaffari, M., Mirzaei, A., and Safayani, M. (2014, January 4\u20136). A novel Bat Algorithm based on chaos for optimization tasks. Proceedings of the 2014 Iranian Conference on Intelligent Systems (ICIS), Bam, Iran.","DOI":"10.1109\/IranianCIS.2014.6802527"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"158508","DOI":"10.1109\/ACCESS.2021.3130933","article-title":"A Weighted Chimp Optimization Algorithm","volume":"9","author":"Khishe","year":"2021","journal-title":"IEEE Access"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Kaur, M., Kaur, R., Singh, N., and Dhiman, G. (2021). SChoA: A newly fusion of sine and cosine with chimp optimization algorithm for HLS of datapaths in digital filters and engineering applications. Eng. Comput., 1\u201329.","DOI":"10.1007\/s00366-020-01233-2"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/s40747-021-00346-5","article-title":"An enhanced chimp optimization algorithm for continuous optimization domains","volume":"8","author":"Jia","year":"2022","journal-title":"Complex Intell. Syst."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"115651","DOI":"10.1016\/j.eswa.2021.115651","article-title":"An efficient multilevel thresholding segmentation method for thermography breast cancer imaging based on improved chimp optimization algorithm","volume":"185","author":"Houssein","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1007\/s12559-021-09933-7","article-title":"Binary Chimp Optimization Algorithm (BChOA): A New Binary Meta-heuristic for Solving Optimization Problems","volume":"13","author":"Wang","year":"2021","journal-title":"Cognit. Comput."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"102764","DOI":"10.1016\/j.bspc.2021.102764","article-title":"Real-time COVID-19 diagnosis from X-Ray images using deep CNN and extreme learning machines stabilized by chimp optimization algorithm","volume":"68","author":"Hu","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Wu, D., Zhang, W., Jia, H., and Leng, X. (2021). Simultaneous Feature Selection and Support Vector Machine Optimization Using an Enhanced Chimp Optimization Algorithm. Algorithms, 14.","DOI":"10.3390\/a14100282"},{"key":"ref_65","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."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Born, M., and Wolf, E. (2019). Principles of Optics: 60th Anniversary Edition, Cambridge University Press. [7th ed.].","DOI":"10.1017\/9781108769914"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.jappgeo.2016.03.027","article-title":"A modified Fuzzy C-Means (FCM) Clustering algorithm and its application on carbonate fluid identification","volume":"129","author":"Liu","year":"2016","journal-title":"J. Appl. Geophy."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.swevo.2011.02.002","article-title":"A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms","volume":"1","author":"Derrac","year":"2011","journal-title":"Swarm Evol. Comput."},{"key":"ref_69","first-page":"291","article-title":"Grey Wolf Algorithm Based on Levy Flight and Random Walk Strategy","volume":"47","author":"Li","year":"2020","journal-title":"Comput. Sci."},{"key":"ref_70","first-page":"16","article-title":"Improved grey wolf optimization algorithm based on iterative mapping and simplex method","volume":"38","author":"Wang","year":"2018","journal-title":"J. Comput. Appl."},{"key":"ref_71","first-page":"1","article-title":"Teaching-learning-based Optimization Algorithm with Social Psychology Theory","volume":"44","author":"He","year":"2021","journal-title":"J. Front. Comput. Sci. Technol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1002\/pri.66","article-title":"The use and interpretation of the Friedman test in the analysis of ordinal-scale data in repeated measures designs","volume":"1","author":"Sheldon","year":"1996","journal-title":"Physiother. Res. Int."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Rey, D., and Neuh\u00e4user, M. (2011). Wilcoxon-signed-rank test. International Encyclopedia of Statistical Science, Springer.","DOI":"10.1007\/978-3-642-04898-2_616"},{"key":"ref_74","unstructured":"(2022, April 07). Introduction to KEEL Software Suite. Available online: https:\/\/sci2s.ugr.es\/keel\/development.php."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.swevo.2015.10.006","article-title":"Performance of Laplacian Biogeography-Based Optimization Algorithm on CEC 2014 continuous optimization benchmarks and camera calibration problem","volume":"27","author":"Garg","year":"2016","journal-title":"Swarm Evol. Comput."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"5573","DOI":"10.1007\/s00500-016-2471-9","article-title":"Since CEC 2005 competition on real-parameter optimisation: A decade of research, progress and comparative analysis\u2019s weakness","volume":"21","author":"Molina","year":"2017","journal-title":"Soft Comput."},{"key":"ref_77","unstructured":"Wu, G., Mallipeddi, R., and Suganthan, P.N. (2017). Problem Definitions and Evaluation Criteria for the CEC 2017 Competition on Constrained Real-Parameter Optimization, Nanyang Technological University. Technical Report."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/6\/189\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:22:47Z","timestamp":1760138567000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/6\/189"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,31]]},"references-count":77,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["a15060189"],"URL":"https:\/\/doi.org\/10.3390\/a15060189","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,31]]}}}