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An improved competitive particle swarm optimization for many-objective optimization problems
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DOI：10.1016/j.eswa.2021.116118.png)
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摘要
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Multi-objective particle swarm optimization (MOPSO) has been widely applied to solve multi-objective optimization 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 particle swarm optimization is proposed for solving manyobjective optimization 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.
Keyword:
Many-objective optimization problems
Many-objective particle swarm optimization
Initialization mechanism
Competitive strategy
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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