Multiobjective optimization inspired by behavior of jellyfish for solving structural design problems

Jui-Sheng Chou and Dinh-Nhat Truong

Chaos, Solitons & Fractals, 2020, vol. 135, issue C

Abstract: 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. The proposed algorithm is tested on 20 multi-objective mathematical benchmark problems, and compared with six well-known metaheuristic optimization algorithms (MOALO, MODA, MOEA/D, MOGWO, MOPSO, and NSGA-II). The results thus obtained indicate that the MOJS finds highly accurate approximations to Pareto-optimal fronts with a uniform distribution of solutions for the test functions. Three constrained structural problems (25-bar tower design, 160-bar tower design, and 942-bar tower design) of minimizing structural weight and maximum nodal deflection were solved using MOJS. The visual analytics demonstrates the merits of MOJS in solving real engineering problems with best Pareto-optimal fronts. Accordingly, the MOJS is an effective and efficient algorithm for solving multi-objective optimization problems.

Keywords: Metaheuristics; Algorithm design; Multi-objective jellyfish search; Pareto dominance; Benchmark functions; Structural design optimization (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:135:y:2020:i:c:s0960077920301405

DOI: 10.1016/j.chaos.2020.109738

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