MOSOA: A new multi-objective seagull optimization algorithm

doi:10.1016/j.eswa.2020.114150

MOSOA: A new multi-objective seagull optimization algorithm

2021-04-01

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DOI：10.1016/j.eswa.2020.114150

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This study introduces the extension of currently developed Seagull Optimization Algorithm (SOA) in terms of multi-objective problems, which is entitled as Multi-objective Seagull Optimization Algorithm (MOSOA). In this algorithm, a concept of dynamic archive is introduced, which has the feature to cache the non-dominated Pareto optimal solutions. The roulette wheel selection approach is utilized to choose the effective archived solutions by simulating the migration and attacking behaviors of seagulls. The proposed algorithm is approved by testing it with twenty-four benchmark test functions, and its performance is compared with existing metaheuristic algorithms. The developed algorithm is analyzed on six constrained problems of engineering design to assess its appropriateness for finding the solutions of real-world problems. The outcomes from the empirical analyzes depict that the proposed algorithm is better than other existing algorithms. The proposed algorithm also considers those Pareto optimal solutions, which demonstrate high convergence.

Keywords:

Convergence

Diversity

Pareto Solutions

Multi-objective Optimization

Seagull Optimization Algorithm

Engineering Design Problems

MOSOA: A new multi-objective seagull optimization algorithm

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