A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean

•An artificial Jellyfish Search (JS) optimizer inspired by jellyfish behavior is proposed.•JS has only two control parameters, which are population size and number of iterations.•The new algorithm is successfully tested on benchmark functions and optimization problems.•JS optimizer outperforms well-...

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Bibliographic Details
Published in:Applied mathematics and computation Vol. 389; p. 125535
Main Authors: Chou, Jui-Sheng, Truong, Dinh-Nhat
Format: Journal Article
Language:English
Published: Elsevier Inc 15.01.2021
Subjects:
Benchmark functions
Bio-inspired swarm intelligence
Design of metaheuristic algorithm
Engineering design
Jellyfish search optimizer
Numerical computation
Benchmark functions
Engineering design
Numerical computation
Jellyfish search optimizer
Bio-inspired swarm intelligence
Design of metaheuristic algorithm
ISSN:0096-3003, 1873-5649
Online Access:Get full text
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Summary:•An artificial Jellyfish Search (JS) optimizer inspired by jellyfish behavior is proposed.•JS has only two control parameters, which are population size and number of iterations.•The new algorithm is successfully tested on benchmark functions and optimization problems.•JS optimizer outperforms well-known metaheuristic algorithms and prior studies. This study develops a novel metaheuristic algorithm that is motivated by the behavior of jellyfish in the ocean and is called artificial Jellyfish Search (JS) optimizer. The simulation of the search behavior of jellyfish involves their following the ocean current, their motions inside a jellyfish swarm (active motions and passive motions), a time control mechanism for switching among these movements, and their convergences into jellyfish bloom. JS optimizer is tested using a comprehensive set of mathematical benchmark functions and applied to a series of structural engineering problems. Fifty small/average-scale and twenty-five large-scale functions involving various dimensions were used to validate JS optimizer, which was compared with ten well-known metaheuristic algorithms. JS optimizer was found to outperform those algorithms in solving mathematical benchmark functions. The JS algorithm was then used to solve structural optimization problems, including 25-bar tower design, 52-bar tower design and 582-bar tower design problems. In those cases, JS not only performed best but also required the fewest evaluations of objective functions. Therefore, JS is potentially an excellent metaheuristic algorithm for solving optimization problems.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2020.125535