Multi-Objective Jellyfish Search (MOJS) Algorithm

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This study develops a Multi-Objective Jellyfish Search (MOJS) algorithm to solve engineering problems optimally with multiple objectives.

https://www.researchgate.net/profile/Jui-Sheng_Chou

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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.

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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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Version	Published	Release Notes
1.0.0	18 Sep 2020		Download

MLA	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.
APA	Chou, J.-S., & Truong, D.-N. (2020). Multiobjective optimization inspired by behavior of jellyfish for solving structural design problems. Chaos, Solitons & Fractals, 135, 109738. Elsevier BV. Retrieved from https://doi.org/10.1016%2Fj.chaos.2020.109738
BibTeX	@article{Chou_2020, doi = {10.1016/j.chaos.2020.109738}, url = {https://doi.org/10.1016%2Fj.chaos.2020.109738}, year = 2020, month = {jun}, publisher = {Elsevier {BV}}, volume = {135}, pages = {109738}, author = {Jui-Sheng Chou and Dinh-Nhat Truong}, title = {Multiobjective optimization inspired by behavior of jellyfish for solving structural design problems}, journal = {Chaos, Solitons {\&} Fractals} }