Multi-objectiveparticleswarmoptimization (MOPSO) has been widely applied to solve multi-objectiveoptimization problems (MOPs), due to its efficient implementation and fast convergence. However, most MOPSOs are ine...
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Multi-objectiveparticleswarmoptimization (MOPSO) has been widely applied to solve multi-objectiveoptimization problems (MOPs), due to its efficient implementation and fast convergence. However, most MOPSOs are ineffective in achieving the balance between convergence and diversity in the high-dimensional objective space. In this paper, an improved competitive particleswarmoptimization is proposed for solving manyobjectiveoptimization problems. To improve the quality of the first generation population, a decision variable dividing-based multi-step initialization mechanism is presented, decision variables are divided into two groups and optimized individually. Moreover, an improved competitive learning strategy is suggested as the main part to further optimization, where particles are updated via leader information from winner particles with well convergence and diversity. The performance of the proposed algorithm is verified by benchmark comparisons with several state-of-the-art evolutionary algorithms. Experimental results demonstrate the promising performance of the algorithm in terms of balance convergence and diversity.
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