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Multi-Objective Jellyfish Search (MOJS) Algorithm

version 1.0.0 (6.91 KB) by nhat truong
This study develops a Multi-Objective Jellyfish Search (MOJS) algorithm to solve engineering problems optimally with multiple objectives.

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Updated 18 Sep 2020

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This study develops a Multi-Objective Jellyfish Search (MOJS) algorithm to solve engineering problems optimally with multiple objectives. Lévy flight, elite population, fixed-size archive, chaotic map, and the opposition-based jumping method are integrated into the MOJS to obtain the Pareto optimal solutions. These techniques are employed to define the motions of jellyfish in an ocean current or a swarm in multi-objective search spaces.

Cite As

Chou, Jui-Sheng, and Dinh-Nhat Truong. “Multiobjective Optimization Inspired by Behavior of Jellyfish for Solving Structural Design Problems.” Chaos, Solitons & Fractals, vol. 135, Elsevier BV, June 2020, p. 109738, doi:10.1016/j.chaos.2020.109738.

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MATLAB Release Compatibility
Created with R2016a
Compatible with any release
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