Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems

•Chameleon Swarm Algorithm (CSA) is benchmarked on 67 benchmark functions.•The exploitation ability of CSA is affirmed by the results on unimodal functions.•The results of CSA on multimodal functions show the exploration ability of CSA.•The results of CSA on composite functions prove the reliability...

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Published in:	Expert systems with applications Vol. 174; p. 114685
Main Author:	Braik, Malik Shehadeh
Format:	Journal Article
Language:	English
Published:	New York Elsevier Ltd 15.07.2021 Elsevier BV
Subjects:	Algorithms Biomimetics Chameleon Swarm Algorithm Comparative studies Design engineering Design optimization Evolutionary algorithms Eye (anatomy) Food Heuristic methods Meta-heuristics Nature-inspired algorithms Optimization Optimization techniques Stability analysis Swamps Swarm intelligence algorithms Swarm intelligence algorithms Optimization techniques Chameleon Swarm Algorithm Evolutionary algorithms Nature-inspired algorithms Meta-heuristics
ISSN:	0957-4174, 1873-6793
Online Access:	Get full text
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Abstract	•Chameleon Swarm Algorithm (CSA) is benchmarked on 67 benchmark functions.•The exploitation ability of CSA is affirmed by the results on unimodal functions.•The results of CSA on multimodal functions show the exploration ability of CSA.•The results of CSA on composite functions prove the reliability level of CSA.•The results on five engineering problems affirm the accuracy of CSA in practice. This paper presents a novel meta-heuristic algorithm named Chameleon Swarm Algorithm (CSA) for solving global numerical optimization problems. The base inspiration for CSA is the dynamic behavior of chameleons when navigating and hunting for food sources on trees, deserts and near swamps. This algorithm mathematically models and implements the behavioral steps of chameleons in their search for food, including their behavior in rotating their eyes to a nearly 360°scope of vision to locate prey and grab prey using their sticky tongues that launch at high speed. These foraging mechanisms practiced by chameleons eventually lead to feasible solutions when applied to address optimization problems. The stability of the proposed algorithm was assessed on sixty-seven benchmark test functions and the performance was examined using several evaluation measures. These test functions involve unimodal, multimodal, hybrid and composition functions with different levels of complexity. An extensive comparative study was conducted to demonstrate the efficacy of CSA over other meta-heuristic algorithms in terms of optimization accuracy. The applicability of the proposed algorithm in reliably addressing real-world problems was demonstrated in solving five constrained and computationally expensive engineering design problems. The overall results of CSA show that it offered a favorable global or near global solution and better performance compared to other meta-heuristics.
AbstractList	This paper presents a novel meta-heuristic algorithm named Chameleon Swarm Algorithm (CSA) for solving global numerical optimization problems. The base inspiration for CSA is the dynamic behavior of chameleons when navigating and hunting for food sources on trees, deserts and near swamps. This algorithm mathematically models and implements the behavioral steps of chameleons in their search for food, including their behavior in rotating their eyes to a nearly 360°scope of vision to locate prey and grab prey using their sticky tongues that launch at high speed. These foraging mechanisms practiced by chameleons eventually lead to feasible solutions when applied to address optimization problems. The stability of the proposed algorithm was assessed on sixty-seven benchmark test functions and the performance was examined using several evaluation measures. These test functions involve unimodal, multimodal, hybrid and composition functions with different levels of complexity. An extensive comparative study was conducted to demonstrate the efficacy of CSA over other meta-heuristic algorithms in terms of optimization accuracy. The applicability of the proposed algorithm in reliably addressing real-world problems was demonstrated in solving five constrained and computationally expensive engineering design problems. The overall results of CSA show that it offered a favorable global or near global solution and better performance compared to other meta-heuristics. •Chameleon Swarm Algorithm (CSA) is benchmarked on 67 benchmark functions.•The exploitation ability of CSA is affirmed by the results on unimodal functions.•The results of CSA on multimodal functions show the exploration ability of CSA.•The results of CSA on composite functions prove the reliability level of CSA.•The results on five engineering problems affirm the accuracy of CSA in practice. This paper presents a novel meta-heuristic algorithm named Chameleon Swarm Algorithm (CSA) for solving global numerical optimization problems. The base inspiration for CSA is the dynamic behavior of chameleons when navigating and hunting for food sources on trees, deserts and near swamps. This algorithm mathematically models and implements the behavioral steps of chameleons in their search for food, including their behavior in rotating their eyes to a nearly 360°scope of vision to locate prey and grab prey using their sticky tongues that launch at high speed. These foraging mechanisms practiced by chameleons eventually lead to feasible solutions when applied to address optimization problems. The stability of the proposed algorithm was assessed on sixty-seven benchmark test functions and the performance was examined using several evaluation measures. These test functions involve unimodal, multimodal, hybrid and composition functions with different levels of complexity. An extensive comparative study was conducted to demonstrate the efficacy of CSA over other meta-heuristic algorithms in terms of optimization accuracy. The applicability of the proposed algorithm in reliably addressing real-world problems was demonstrated in solving five constrained and computationally expensive engineering design problems. The overall results of CSA show that it offered a favorable global or near global solution and better performance compared to other meta-heuristics.
ArticleNumber	114685
Author	Braik, Malik Shehadeh
Author_xml	– sequence: 1 givenname: Malik Shehadeh surname: Braik fullname: Braik, Malik Shehadeh email: mbraik@bau.edu.jo organization: Department of Computer Science, Al-Balqa Applied University, Al-salt, Jordan
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Cites_doi	10.1109/TSMCB.2009.2015956 10.1016/S0262-4079(08)60843-X 10.1109/MHS.1995.494215 10.1016/j.advengsoft.2013.12.007 10.1023/A:1022602019183 10.1109/2.781637 10.1242/jeb.203.21.3255 10.1016/j.knosys.2019.105190 10.1016/j.engappai.2020.103541 10.1126/science.220.4598.671 10.1093/oso/9780195131581.001.0001 10.1016/j.asoc.2013.05.010 10.1016/j.advengsoft.2017.05.014 10.1109/TEVC.2010.2059031 10.1109/4235.930318 10.1109/4235.585893 10.1016/j.knosys.2015.07.006 10.1016/j.advengsoft.2017.07.002 10.1504/IJBIC.2010.032124 10.1016/j.eswa.2017.04.033 10.1016/j.knosys.2018.11.024 10.17535/crorr.2019.0015 10.1016/j.scient.2012.04.009 10.1115/1.2912596 10.1016/j.ins.2009.03.004 10.1016/j.knosys.2015.12.022 10.1115/1.2919393 10.1177/003754970107600201 10.70594/brain/v10.i1/10 10.3897/vz.65.e31518 10.1155/2018/3967457 10.1080/03610918.2014.931971 10.1016/j.ins.2014.02.123 10.1007/s00521-013-1498-4 10.1016/j.swevo.2018.02.013 10.1080/00207160108805080 10.1109/CEC.2017.7969336 10.1016/j.asoc.2015.03.003 10.1007/s00521-015-1870-7 10.1007/s00170-019-03621-5
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References	Mirjalili (b0175) 2016; 96 Faramarzi, Heidarinejad, Stephens, Mirjalili (b0075) 2020; 191 Braik, Al-Zoubi, Al-Hiary (b0015) 2020 Rodríguez, N., Gupta, A., Zabala, P. L., & Cabrera-Guerrero, G. (2018). Optimization algorithms combining (meta) heuristics and mathematical programming and its application in engineering. Kennedy, Eberhart (b0130) 1995 Demšar (b0045) 2006; 7 Faramarzi, Afshar (b0070) 2012; 19 Uymaz, Tezel, Yel (b0235) 2015; 31 Pereira, Afonso, Medeiros (b0200) 2015; 44 Glaw (b0090) 2015; 65 Rashedi, Nezamabadi-Pour, Saryazdi (b0210) 2009; 179 Braik, Sheta, Turabieh, Alhiary (b0025) 2020 Rechenberg (b0215) 1973; 104 Braik, Sheta, Al-Hiary (b0020) 2020 Holm (b0105) 1979 Mirjalili, Mirjalili, Lewis (b0190) 2014; 69 (2), 60–68. Mirjalili, Mirjalili, Hatamlou (b0185) 2016; 27 Wang, Guo, Gandomi, Hao, Wang (b0240) 2014; 274 Mirjalili (b0170) 2015; 89 Zhan, Z. H., Zhang, J., Li, Y., & Chung, H. S. H. (2009). Adaptive particle swarm optimization. Sree Ranjini, K. S., & Murugan, S. (2017). Memory based hybrid dragonfly algorithm for numerical optimization problems. Kannan, Kramer (b0115) 1994; 116 (pp. 39–43). . Young, E. (2008). Chameleons fine-tune camouflage to predator’s vision. Awad, N. H., Ali, M. Z. & Suganthan, P. N. (2017). Ensemble sinusoidal differential covariance matrix adaptation with euclidean neighborhood for solving cec2017 benchmark problems. In Herrel, Meyers, Aerts, Nishikawa (b0100) 2000; 203 Karypis, Han, Kumar (b0120) 1999; 32 Mirjalili, Gandomi, Mirjalili, Saremi, Faris, Mirjalili (b0180) 2017; 114 Chumburidze, Basheleishvili, Khetsuriani (b0035) 2019; 10 63–78. Meraihi, Gabis, Ramdane-Cherif, Acheli (b0160) 2020 Qi, Jin, Wang, Xiao, Zhang (b0205) 2019 Sheta, Braik, Al-Hiary (b0230) 2019; 103 Mlinarić, Perić, Matejaš (b0195) 2019 Yang, Deb, Loomes, Karamanoglu (b0260) 2013; 23 Wolpert, Macready (b0245) 1997; 1 Kirkpatrick, S., Gelatt, C. D. & Vecchi, M. P. (1983). Optimization by simulated annealing. Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: Harmony search. Bonabeau, E., Dorigo, M., Marco, D. d. R.D.F., Theraulaz, G., & Théraulaz, G. (1999). Swarm intelligence: From natural to artificial systems (No 1). Oxford university press. Liu, Mernik, HrnčIč, Črepinšek (b0150) 2013; 13 Luenberger, Ye (b0155) 1984 (4598), 671–680. Sandgren, E. (1990). Nonlinear integer and discrete programming in mechanical design optimization. (pp. 372–379). Dhiman, Kumar (b0055) 2019; 165 Gandomi, Yang (b0080) 2011 Jain, Singh, Rani (b0110) 2019; 44 arXiv preprint arXiv:1003.1409. Yang (b0255) 2010 Koppen, Wolpert, Macready (b0140) 2001; 53 Yang, X. S. (2010). (6), 1362–1381. Mezura-Montes, Coello (b0165) 2005 Chen, Q., Liu, B., Zhang, Q., Liang, J., Suganthan, P., & Qu, B. (2014). Problem definitions and evaluation criteria for cec 2015 special session on bound constrained single-objective computationally expensive numerical optimization. Technical Report, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Technical Report, Nanyang Technological University. Kaur, Awasthi, Sangal, Dhiman (b0125) 2020; 90 Das, Suganthan (b0040) 2011; 15 Dhiman, Kumar (b0050) 2017; 114 Goldberg, Holland (b0095) 1988; 3 Digalakis, Margaritis (b0060) 2001; 77 Goldberg (10.1016/j.eswa.2021.114685_b0095) 1988; 3 Demšar (10.1016/j.eswa.2021.114685_b0045) 2006; 7 Mirjalili (10.1016/j.eswa.2021.114685_b0180) 2017; 114 Herrel (10.1016/j.eswa.2021.114685_b0100) 2000; 203 Koppen (10.1016/j.eswa.2021.114685_b0140) 2001; 53 Chumburidze (10.1016/j.eswa.2021.114685_b0035) 2019; 10 10.1016/j.eswa.2021.114685_b0085 Mirjalili (10.1016/j.eswa.2021.114685_b0170) 2015; 89 Kaur (10.1016/j.eswa.2021.114685_b0125) 2020; 90 Yang (10.1016/j.eswa.2021.114685_b0255) 2010 Faramarzi (10.1016/j.eswa.2021.114685_b0075) 2020; 191 Mirjalili (10.1016/j.eswa.2021.114685_b0185) 2016; 27 Faramarzi (10.1016/j.eswa.2021.114685_b0070) 2012; 19 Kennedy (10.1016/j.eswa.2021.114685_b0130) 1995 Karypis (10.1016/j.eswa.2021.114685_b0120) 1999; 32 Dhiman (10.1016/j.eswa.2021.114685_b0050) 2017; 114 Braik (10.1016/j.eswa.2021.114685_b0025) 2020 10.1016/j.eswa.2021.114685_b0225 Mezura-Montes (10.1016/j.eswa.2021.114685_b0165) 2005 Kannan (10.1016/j.eswa.2021.114685_b0115) 1994; 116 Luenberger (10.1016/j.eswa.2021.114685_b0155) 1984 Jain (10.1016/j.eswa.2021.114685_b0110) 2019; 44 10.1016/j.eswa.2021.114685_b0270 10.1016/j.eswa.2021.114685_b0030 Glaw (10.1016/j.eswa.2021.114685_b0090) 2015; 65 Liu (10.1016/j.eswa.2021.114685_b0150) 2013; 13 Das (10.1016/j.eswa.2021.114685_b0040) 2011; 15 Wang (10.1016/j.eswa.2021.114685_b0240) 2014; 274 Dhiman (10.1016/j.eswa.2021.114685_b0055) 2019; 165 Qi (10.1016/j.eswa.2021.114685_b0205) 2019 Pereira (10.1016/j.eswa.2021.114685_b0200) 2015; 44 Uymaz (10.1016/j.eswa.2021.114685_b0235) 2015; 31 10.1016/j.eswa.2021.114685_b0135 10.1016/j.eswa.2021.114685_b0065 Braik (10.1016/j.eswa.2021.114685_b0015) 2020 10.1016/j.eswa.2021.114685_b0220 10.1016/j.eswa.2021.114685_b0265 10.1016/j.eswa.2021.114685_b0145 Rechenberg (10.1016/j.eswa.2021.114685_b0215) 1973; 104 Yang (10.1016/j.eswa.2021.114685_b0260) 2013; 23 Gandomi (10.1016/j.eswa.2021.114685_b0080) 2011 Holm (10.1016/j.eswa.2021.114685_b0105) 1979 Digalakis (10.1016/j.eswa.2021.114685_b0060) 2001; 77 Sheta (10.1016/j.eswa.2021.114685_b0230) 2019; 103 Mlinarić (10.1016/j.eswa.2021.114685_b0195) 2019 Braik (10.1016/j.eswa.2021.114685_b0020) 2020 Rashedi (10.1016/j.eswa.2021.114685_b0210) 2009; 179 Mirjalili (10.1016/j.eswa.2021.114685_b0190) 2014; 69 10.1016/j.eswa.2021.114685_b0005 10.1016/j.eswa.2021.114685_b0010 Wolpert (10.1016/j.eswa.2021.114685_b0245) 1997; 1 10.1016/j.eswa.2021.114685_b0250 Meraihi (10.1016/j.eswa.2021.114685_b0160) 2020 Mirjalili (10.1016/j.eswa.2021.114685_b0175) 2016; 96
References_xml	– year: 2019 ident: b0205 article-title: Complex-valued discrete-time neural dynamics for perturbed time-dependent complex quadratic programming with applications publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 31 start-page: 153 year: 2015 end-page: 171 ident: b0235 article-title: Artificial algae algorithm (aaa) for nonlinear global optimization publication-title: Applied Soft Computing – reference: Geem, Z. W., Kim, J. H., & Loganathan, G. V. (2001). A new heuristic optimization algorithm: Harmony search. – start-page: 1 year: 2020 end-page: 25 ident: b0015 article-title: Artificial neural networks training via bio-inspired optimisation algorithms: Modelling industrial winding process, case study publication-title: Soft Computing – volume: 44 start-page: 2636 year: 2015 end-page: 2653 ident: b0200 article-title: Overview of friedman’s test and post-hoc analysis publication-title: Communications in Statistics-Simulation and Computation – reference: Kirkpatrick, S., Gelatt, C. D. & Vecchi, M. P. (1983). Optimization by simulated annealing. – volume: 53 start-page: 295 year: 2001 end-page: 296 ident: b0140 article-title: Remarks on a recent paper on the no free lunch theorems publication-title: IEEE Transactions on Evolutionary Computation – reference: Sandgren, E. (1990). Nonlinear integer and discrete programming in mechanical design optimization. – volume: 19 start-page: 373 year: 2012 end-page: 380 ident: b0070 article-title: Application of cellular automata to size and topology optimization of truss structures publication-title: Scientia Iranica – reference: (6), 1362–1381. – reference: Chen, Q., Liu, B., Zhang, Q., Liang, J., Suganthan, P., & Qu, B. (2014). Problem definitions and evaluation criteria for cec 2015 special session on bound constrained single-objective computationally expensive numerical optimization. Technical Report, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Technical Report, Nanyang Technological University. – reference: , 63–78. – volume: 65 start-page: 167 year: 2015 end-page: 246 ident: b0090 article-title: Taxonomic checklist of chameleons (Squamata: Chamaeleonidae) Taxonomic checklist of chameleons (squamata: Chamaeleonidae) publication-title: Vertebrate Zoology – volume: 23 start-page: 2051 year: 2013 end-page: 2057 ident: b0260 article-title: A framework for self-tuning optimization algorithm publication-title: Neural Computing and Applications – reference: Bonabeau, E., Dorigo, M., Marco, D. d. R.D.F., Theraulaz, G., & Théraulaz, G. (1999). Swarm intelligence: From natural to artificial systems (No 1). Oxford university press. – reference: Young, E. (2008). Chameleons fine-tune camouflage to predator’s vision. – start-page: 1942 year: 1995 end-page: 1948 ident: b0130 article-title: Particle swarm optimization publication-title: Proceedings of icnn’95-international conference on neural networks, 4 – volume: 3 start-page: 95 year: 1988 end-page: 99 ident: b0095 article-title: Genetic algorithms and machine learning publication-title: Machine Learning – volume: 165 start-page: 169 year: 2019 end-page: 196 ident: b0055 article-title: Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems publication-title: Knowledge-Based Systems – year: 2010 ident: b0255 article-title: Nature-inspired metaheuristic algorithms – volume: 7 start-page: 1 year: 2006 end-page: 30 ident: b0045 article-title: Statistical comparisons of classifiers over multiple data sets publication-title: Journal of Machine Learning Research – reference: Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In – volume: 1 start-page: 67 year: 1997 end-page: 82 ident: b0245 article-title: No free lunch theorems for optimization publication-title: IEEE Transactions on Evolutionary Computation – reference: (4598), 671–680. – volume: 179 start-page: 2232 year: 2009 end-page: 2248 ident: b0210 article-title: Gsa: A gravitational search algorithm publication-title: Information Sciences – volume: 15 start-page: 4 year: 2011 end-page: 31 ident: b0040 article-title: Differential evolution: A survey of the state-of-the-art publication-title: IEEE Transactions on Evolutionary Computation – volume: 116 start-page: 405 year: 1994 end-page: 411 ident: b0115 article-title: An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design publication-title: Journal of Mechanical Design – reference: Yang, X. S. (2010). – start-page: 652 year: 2005 end-page: 662 ident: b0165 article-title: Useful infeasible solutions in engineering optimization with evolutionary algorithms publication-title: Mexican international conference on artificial intelligence – year: 1984 ident: b0155 article-title: Linear and nonlinear programming (2) – volume: 27 start-page: 495 year: 2016 end-page: 513 ident: b0185 article-title: Multi-verse optimizer: A nature-inspired algorithm for global optimization publication-title: Neural Computing and Applications – volume: 191 year: 2020 ident: b0075 article-title: Equilibrium optimizer: A novel optimization algorithm publication-title: Knowledge-Based Systems – reference: (pp. 39–43). – reference: Awad, N. H., Ali, M. Z. & Suganthan, P. N. (2017). Ensemble sinusoidal differential covariance matrix adaptation with euclidean neighborhood for solving cec2017 benchmark problems. In – start-page: 1 year: 2020 end-page: 26 ident: b0025 article-title: A novel lifetime scheme for enhancing the convergence performance of salp swarm algorithm publication-title: Soft Computing – volume: 32 start-page: 68 year: 1999 end-page: 75 ident: b0120 article-title: Chameleon: Hierarchical clustering using dynamic modeling publication-title: Computer – start-page: 65 year: 1979 end-page: 70 ident: b0105 article-title: A simple sequentially rejective multiple test procedure publication-title: Scandinavian Journal of Statistics – volume: 10 start-page: 101 year: 2019 end-page: 107 ident: b0035 article-title: Dynamic programming and greedy algorithm strategy for solving several classes of graph optimization problems publication-title: BRAIN. Broad Research in Artificial Intelligence and Neuroscience – reference: Rodríguez, N., Gupta, A., Zabala, P. L., & Cabrera-Guerrero, G. (2018). Optimization algorithms combining (meta) heuristics and mathematical programming and its application in engineering. – reference: Zhan, Z. H., Zhang, J., Li, Y., & Chung, H. S. H. (2009). Adaptive particle swarm optimization. – reference: Sree Ranjini, K. S., & Murugan, S. (2017). Memory based hybrid dragonfly algorithm for numerical optimization problems. – reference: (pp. 372–379). – volume: 89 start-page: 228 year: 2015 end-page: 249 ident: b0170 article-title: Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm publication-title: Knowledge-Based Systems – year: 2011 ident: b0080 article-title: Computational optimization, methods and algorithms (259–281) – volume: 69 start-page: 46 year: 2014 end-page: 61 ident: b0190 article-title: Grey wolf optimizer publication-title: Advances in Engineering Software – volume: 103 start-page: 1359 year: 2019 end-page: 1380 ident: b0230 article-title: Modeling the tennessee eastman chemical process reactor using bio-inspired feedforward neural network (bi-ff-nn) publication-title: The International Journal of Advanced Manufacturing Technology – volume: 96 start-page: 120 year: 2016 end-page: 133 ident: b0175 article-title: Sca: A sine cosine algorithm for solving optimization problems publication-title: Knowledge-Based Systems – volume: 274 start-page: 17 year: 2014 end-page: 34 ident: b0240 article-title: Chaotic krill herd algorithm publication-title: Information Sciences – start-page: 1 year: 2020 end-page: 48 ident: b0160 article-title: A comprehensive survey of Crow Search Algorithm and its applications publication-title: Artificial Intelligence Review – reference: . arXiv preprint arXiv:1003.1409. – volume: 90 year: 2020 ident: b0125 article-title: Tunicate swarm algorithm: A new bio-inspired based metaheuristic paradigm for global optimization publication-title: Engineering Applications of Artificial Intelligence – volume: 104 start-page: 15 year: 1973 end-page: 16 ident: b0215 article-title: Evolution strategy: Optimization of technical systems by means of biological evolution publication-title: Fromman-Holzboog, Stuttgart – reference: . – reference: (2), 60–68. – volume: 44 start-page: 148 year: 2019 end-page: 175 ident: b0110 article-title: A novel nature-inspired algorithm for optimization: Squirrel search algorithm publication-title: Swarm and Evolutionary Computation – volume: 77 start-page: 481 year: 2001 end-page: 506 ident: b0060 article-title: On benchmarking functions for genetic algorithms publication-title: International Journal of Computer Mathematics – start-page: 1 year: 2020 end-page: 33 ident: b0020 article-title: A novel meta-heuristic search algorithm for solving optimization problems: Capuchin search algorithm publication-title: Neural Computing and Applications – volume: 203 start-page: 3255 year: 2000 end-page: 3263 ident: b0100 article-title: The mechanics of prey prehension in chameleons publication-title: Journal of Experimental Biology – volume: 114 start-page: 48 year: 2017 end-page: 70 ident: b0050 article-title: Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications publication-title: Advances in Engineering Software – volume: 13 start-page: 3792 year: 2013 end-page: 3805 ident: b0150 article-title: A parameter control method of evolutionary algorithms using exploration and exploitation measures with a practical application for fitting sovova’s mass transfer model publication-title: Applied Soft Computing – start-page: 165 year: 2019 end-page: 174 ident: b0195 article-title: Multi-objective programming methodology for solving economic diplomacy resource allocation problem publication-title: Croatian Operational Research Review – volume: 114 start-page: 163 year: 2017 end-page: 191 ident: b0180 article-title: Salp swarm algorithm: A bio-inspired optimizer for engineering design problems publication-title: Advances in Engineering Software – ident: 10.1016/j.eswa.2021.114685_b0270 doi: 10.1109/TSMCB.2009.2015956 – ident: 10.1016/j.eswa.2021.114685_b0265 doi: 10.1016/S0262-4079(08)60843-X – ident: 10.1016/j.eswa.2021.114685_b0065 doi: 10.1109/MHS.1995.494215 – volume: 69 start-page: 46 year: 2014 ident: 10.1016/j.eswa.2021.114685_b0190 article-title: Grey wolf optimizer publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2013.12.007 – year: 2011 ident: 10.1016/j.eswa.2021.114685_b0080 – volume: 3 start-page: 95 issue: 2 year: 1988 ident: 10.1016/j.eswa.2021.114685_b0095 article-title: Genetic algorithms and machine learning publication-title: Machine Learning doi: 10.1023/A:1022602019183 – volume: 32 start-page: 68 issue: 8 year: 1999 ident: 10.1016/j.eswa.2021.114685_b0120 article-title: Chameleon: Hierarchical clustering using dynamic modeling publication-title: Computer doi: 10.1109/2.781637 – volume: 203 start-page: 3255 issue: 21 year: 2000 ident: 10.1016/j.eswa.2021.114685_b0100 article-title: The mechanics of prey prehension in chameleons publication-title: Journal of Experimental Biology doi: 10.1242/jeb.203.21.3255 – volume: 191 year: 2020 ident: 10.1016/j.eswa.2021.114685_b0075 article-title: Equilibrium optimizer: A novel optimization algorithm publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2019.105190 – volume: 90 year: 2020 ident: 10.1016/j.eswa.2021.114685_b0125 article-title: Tunicate swarm algorithm: A new bio-inspired based metaheuristic paradigm for global optimization publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2020.103541 – ident: 10.1016/j.eswa.2021.114685_b0135 doi: 10.1126/science.220.4598.671 – ident: 10.1016/j.eswa.2021.114685_b0010 doi: 10.1093/oso/9780195131581.001.0001 – volume: 13 start-page: 3792 issue: 9 year: 2013 ident: 10.1016/j.eswa.2021.114685_b0150 article-title: A parameter control method of evolutionary algorithms using exploration and exploitation measures with a practical application for fitting sovova’s mass transfer model publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2013.05.010 – volume: 114 start-page: 48 year: 2017 ident: 10.1016/j.eswa.2021.114685_b0050 article-title: Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2017.05.014 – volume: 15 start-page: 4 issue: 1 year: 2011 ident: 10.1016/j.eswa.2021.114685_b0040 article-title: Differential evolution: A survey of the state-of-the-art publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2010.2059031 – year: 2019 ident: 10.1016/j.eswa.2021.114685_b0205 article-title: Complex-valued discrete-time neural dynamics for perturbed time-dependent complex quadratic programming with applications publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 53 start-page: 295 year: 2001 ident: 10.1016/j.eswa.2021.114685_b0140 article-title: Remarks on a recent paper on the no free lunch theorems publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.930318 – volume: 1 start-page: 67 issue: 1 year: 1997 ident: 10.1016/j.eswa.2021.114685_b0245 article-title: No free lunch theorems for optimization publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.585893 – volume: 89 start-page: 228 year: 2015 ident: 10.1016/j.eswa.2021.114685_b0170 article-title: Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2015.07.006 – volume: 114 start-page: 163 year: 2017 ident: 10.1016/j.eswa.2021.114685_b0180 article-title: Salp swarm algorithm: A bio-inspired optimizer for engineering design problems publication-title: Advances in Engineering Software doi: 10.1016/j.advengsoft.2017.07.002 – ident: 10.1016/j.eswa.2021.114685_b0250 doi: 10.1504/IJBIC.2010.032124 – start-page: 65 year: 1979 ident: 10.1016/j.eswa.2021.114685_b0105 article-title: A simple sequentially rejective multiple test procedure publication-title: Scandinavian Journal of Statistics – start-page: 1 year: 2020 ident: 10.1016/j.eswa.2021.114685_b0160 article-title: A comprehensive survey of Crow Search Algorithm and its applications publication-title: Artificial Intelligence Review – ident: 10.1016/j.eswa.2021.114685_b0145 doi: 10.1016/j.eswa.2017.04.033 – volume: 165 start-page: 169 year: 2019 ident: 10.1016/j.eswa.2021.114685_b0055 article-title: Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2018.11.024 – start-page: 165 year: 2019 ident: 10.1016/j.eswa.2021.114685_b0195 article-title: Multi-objective programming methodology for solving economic diplomacy resource allocation problem publication-title: Croatian Operational Research Review doi: 10.17535/crorr.2019.0015 – volume: 19 start-page: 373 issue: 3 year: 2012 ident: 10.1016/j.eswa.2021.114685_b0070 article-title: Application of cellular automata to size and topology optimization of truss structures publication-title: Scientia Iranica doi: 10.1016/j.scient.2012.04.009 – ident: 10.1016/j.eswa.2021.114685_b0225 doi: 10.1115/1.2912596 – ident: 10.1016/j.eswa.2021.114685_b0030 – volume: 179 start-page: 2232 issue: 13 year: 2009 ident: 10.1016/j.eswa.2021.114685_b0210 article-title: Gsa: A gravitational search algorithm publication-title: Information Sciences doi: 10.1016/j.ins.2009.03.004 – start-page: 652 year: 2005 ident: 10.1016/j.eswa.2021.114685_b0165 article-title: Useful infeasible solutions in engineering optimization with evolutionary algorithms – volume: 96 start-page: 120 year: 2016 ident: 10.1016/j.eswa.2021.114685_b0175 article-title: Sca: A sine cosine algorithm for solving optimization problems publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2015.12.022 – volume: 116 start-page: 405 issue: 2 year: 1994 ident: 10.1016/j.eswa.2021.114685_b0115 article-title: An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design publication-title: Journal of Mechanical Design doi: 10.1115/1.2919393 – ident: 10.1016/j.eswa.2021.114685_b0085 doi: 10.1177/003754970107600201 – volume: 10 start-page: 101 issue: 1 year: 2019 ident: 10.1016/j.eswa.2021.114685_b0035 article-title: Dynamic programming and greedy algorithm strategy for solving several classes of graph optimization problems publication-title: BRAIN. Broad Research in Artificial Intelligence and Neuroscience doi: 10.70594/brain/v10.i1/10 – volume: 65 start-page: 167 issue: 2 year: 2015 ident: 10.1016/j.eswa.2021.114685_b0090 article-title: Taxonomic checklist of chameleons (Squamata: Chamaeleonidae) Taxonomic checklist of chameleons (squamata: Chamaeleonidae) publication-title: Vertebrate Zoology doi: 10.3897/vz.65.e31518 – volume: 7 start-page: 1 year: 2006 ident: 10.1016/j.eswa.2021.114685_b0045 article-title: Statistical comparisons of classifiers over multiple data sets publication-title: Journal of Machine Learning Research – start-page: 1 year: 2020 ident: 10.1016/j.eswa.2021.114685_b0025 article-title: A novel lifetime scheme for enhancing the convergence performance of salp swarm algorithm publication-title: Soft Computing – year: 1984 ident: 10.1016/j.eswa.2021.114685_b0155 – ident: 10.1016/j.eswa.2021.114685_b0220 doi: 10.1155/2018/3967457 – volume: 44 start-page: 2636 issue: 10 year: 2015 ident: 10.1016/j.eswa.2021.114685_b0200 article-title: Overview of friedman’s test and post-hoc analysis publication-title: Communications in Statistics-Simulation and Computation doi: 10.1080/03610918.2014.931971 – volume: 274 start-page: 17 year: 2014 ident: 10.1016/j.eswa.2021.114685_b0240 article-title: Chaotic krill herd algorithm publication-title: Information Sciences doi: 10.1016/j.ins.2014.02.123 – volume: 23 start-page: 2051 issue: 7–8 year: 2013 ident: 10.1016/j.eswa.2021.114685_b0260 article-title: A framework for self-tuning optimization algorithm publication-title: Neural Computing and Applications doi: 10.1007/s00521-013-1498-4 – volume: 44 start-page: 148 year: 2019 ident: 10.1016/j.eswa.2021.114685_b0110 article-title: A novel nature-inspired algorithm for optimization: Squirrel search algorithm publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2018.02.013 – volume: 77 start-page: 481 issue: 4 year: 2001 ident: 10.1016/j.eswa.2021.114685_b0060 article-title: On benchmarking functions for genetic algorithms publication-title: International Journal of Computer Mathematics doi: 10.1080/00207160108805080 – volume: 104 start-page: 15 year: 1973 ident: 10.1016/j.eswa.2021.114685_b0215 article-title: Evolution strategy: Optimization of technical systems by means of biological evolution publication-title: Fromman-Holzboog, Stuttgart – ident: 10.1016/j.eswa.2021.114685_b0005 doi: 10.1109/CEC.2017.7969336 – volume: 31 start-page: 153 year: 2015 ident: 10.1016/j.eswa.2021.114685_b0235 article-title: Artificial algae algorithm (aaa) for nonlinear global optimization publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2015.03.003 – start-page: 1942 year: 1995 ident: 10.1016/j.eswa.2021.114685_b0130 article-title: Particle swarm optimization – volume: 27 start-page: 495 issue: 2 year: 2016 ident: 10.1016/j.eswa.2021.114685_b0185 article-title: Multi-verse optimizer: A nature-inspired algorithm for global optimization publication-title: Neural Computing and Applications doi: 10.1007/s00521-015-1870-7 – volume: 103 start-page: 1359 issue: 1–4 year: 2019 ident: 10.1016/j.eswa.2021.114685_b0230 article-title: Modeling the tennessee eastman chemical process reactor using bio-inspired feedforward neural network (bi-ff-nn) publication-title: The International Journal of Advanced Manufacturing Technology doi: 10.1007/s00170-019-03621-5 – year: 2010 ident: 10.1016/j.eswa.2021.114685_b0255 – start-page: 1 year: 2020 ident: 10.1016/j.eswa.2021.114685_b0015 article-title: Artificial neural networks training via bio-inspired optimisation algorithms: Modelling industrial winding process, case study publication-title: Soft Computing – start-page: 1 year: 2020 ident: 10.1016/j.eswa.2021.114685_b0020 article-title: A novel meta-heuristic search algorithm for solving optimization problems: Capuchin search algorithm publication-title: Neural Computing and Applications
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Snippet	•Chameleon Swarm Algorithm (CSA) is benchmarked on 67 benchmark functions.•The exploitation ability of CSA is affirmed by the results on unimodal... This paper presents a novel meta-heuristic algorithm named Chameleon Swarm Algorithm (CSA) for solving global numerical optimization problems. The base...
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SubjectTerms	Algorithms Biomimetics Chameleon Swarm Algorithm Comparative studies Design engineering Design optimization Evolutionary algorithms Eye (anatomy) Food Heuristic methods Meta-heuristics Nature-inspired algorithms Optimization Optimization techniques Stability analysis Swamps Swarm intelligence algorithms
Title	Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems
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