A convergence and diversity guided leader selection strategy for many-objective particle swarm optimization. (October 2022) - Li, Lingjie; Li, Yongfeng; Lin, Qiuzhen; Ming, Zhong; Coello, Carlos A. Coello
Abstract: Recently, particle swarm optimizer (PSO) is extended to solve many-objective optimization problems (MaOPs) and becomes a hot research topic in the field of evolutionary computation. Particularly, the leader particle selection (LPS) and the search direction used in a velocity update strategy are two crucial factors in PSOs. However, the LPS strategies for most existing PSOs are not so efficient in high-dimensional objective space, mainly due to the lack of convergence pressure or loss of diversity. In order to address these two issues and improve the performance of PSO in high-dimensional objective space, this paper proposes a convergence and diversity guided leader selection strategy for PSO, denoted as CDLS, in which different leader particles are adaptively selected for each particle based on its corresponding situation of convergence and diversity. In this way, a good tradeoff between the convergence and diversity can be achieved by CDLS. To verify the effectiveness of CDLS, it is embedded into the PSO search process of three well-known PSOs. Furthermore, a new variant of PSO combining with the CDLS strategy, namely PSO/CDLS, is also presented. The experimental results validate the superiority of our proposed CDLS strategy and the effectiveness of PSO/CDLS, when solving numerous MaOPs with regular and irregular Pareto fronts ( PF s). Highlights: This paper proposes a convergence and diversity guided leader selection strategy for many objective PSO. CDLS strategy takes the status of both convergence and diversity into consideration for selecting leader particles. Different leader particles are adaptively selected according to the status of convergence and diversity. Our proposed method shows the superiority over six advanced MOEAs when solving numerous MaOPs.