Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization

This paper introduces a bio-inspired metaheuristic optimization algorithm named Tunicate Swarm Algorithm (TSA). The proposed algorithm imitates jet propulsion and swarm behaviors of tunicates during the navigation and foraging process. The performance of TSA is evaluated on seventy-four benchmark te...

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Bibliographic Details
Published inEngineering applications of artificial intelligence Vol. 90; p. 103541
Main Authors Kaur, Satnam, Awasthi, Lalit K., Sangal, A.L., Dhiman, Gaurav
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.04.2020
Subjects
Constrained optimization
Engineering design problems
Metaheuristics
Swarm intelligence
Unconstrained optimization
Swarm intelligence
Unconstrained optimization
Engineering design problems
Metaheuristics
Constrained optimization
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Summary:This paper introduces a bio-inspired metaheuristic optimization algorithm named Tunicate Swarm Algorithm (TSA). The proposed algorithm imitates jet propulsion and swarm behaviors of tunicates during the navigation and foraging process. The performance of TSA is evaluated on seventy-four benchmark test problems employing sensitivity, convergence and scalability analysis along with ANOVA test. The efficacy of this algorithm is further compared with several well-regarded metaheuristic approaches based on the generated optimal solutions. In addition, we also executed the proposed algorithm on six constrained and one unconstrained engineering design problems to further verify its robustness. The simulation results demonstrate that TSA generates better optimal solutions in comparison to other competitive algorithms and is capable of solving real case studies having unknown search spaces. Note that the source codes of the proposed TSA algorithm are available at
ISSN:0952-1976
1873-6769
DOI:10.1016/j.engappai.2020.103541