To overcome premature convergence and the diversity problem in multi-objective optimization, a multi-strategy multi-objective particle swarm optimization algorithm was proposed in this study. In the MS-MOPSO algorithm, two complementary sub-swarms, which were based on PSO and GWO optimization techniques, were used. Two sub-swarms were optimized by applying different update rules, but they exchanged information through an external archive based on the Pareto dominance concept. In the proposed algorithm, the solutions in the archive were determined through the Pareto dominance principle, and the permutation-averaged discrete Fréchet distance of each normalized objective vector to an ideal profile obtained using the external archive was utilized for the leader selection process. To improve the local search of the solutions without changing the position and meaning of continuous decision vectors, three different neighborhood concepts, which included single-variable, subset, and full vector perturbations, were used. This proposed method was applied to three objective DTLZ1 to DTLZ7 test functions and was compared with MOPSO, MOGWO, and SPEA2 methods. According to the results, good convergence was found for DTLZ2 and DTLZ4 test functions, while diversity and coverage were achieved for some test functions, even though the proposed approach was not superior to other approaches for all test functions. From the results, it can be seen that using the proposed approach for continuous multi-objective optimization is promising.
Citation: Zhe Luo, Guang-Yu Zhu, Zhi-feng Yang, Si-jie Wu, Xin-Yu Wang. Study on multi-objective particle swarm optimization algorithm combining multiple strategies[J]. Journal of Industrial and Management Optimization, 2026, 22(9): 4514-4546. doi: 10.3934/jimo.2026157
To overcome premature convergence and the diversity problem in multi-objective optimization, a multi-strategy multi-objective particle swarm optimization algorithm was proposed in this study. In the MS-MOPSO algorithm, two complementary sub-swarms, which were based on PSO and GWO optimization techniques, were used. Two sub-swarms were optimized by applying different update rules, but they exchanged information through an external archive based on the Pareto dominance concept. In the proposed algorithm, the solutions in the archive were determined through the Pareto dominance principle, and the permutation-averaged discrete Fréchet distance of each normalized objective vector to an ideal profile obtained using the external archive was utilized for the leader selection process. To improve the local search of the solutions without changing the position and meaning of continuous decision vectors, three different neighborhood concepts, which included single-variable, subset, and full vector perturbations, were used. This proposed method was applied to three objective DTLZ1 to DTLZ7 test functions and was compared with MOPSO, MOGWO, and SPEA2 methods. According to the results, good convergence was found for DTLZ2 and DTLZ4 test functions, while diversity and coverage were achieved for some test functions, even though the proposed approach was not superior to other approaches for all test functions. From the results, it can be seen that using the proposed approach for continuous multi-objective optimization is promising.
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Zhe Luo, Guang-Yu Zhu, Zhi-feng Yang, Si-jie Wu, Xin-Yu Wang. Study on multi-objective particle swarm optimization algorithm combining multiple strategies[J]. Journal of Industrial and Management Optimization, 2026, 22(9): 4514-4546. doi: 10.3934/jimo.2026157
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