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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Vydáno v:Expert systems with applications Ročník 174; s. 114685
Hlavní autor: Braik, Malik Shehadeh
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 15.07.2021
Elsevier BV
Témata:
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
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Shrnutí:•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.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2021.114685