A two-stage maintenance and multi-strategy selection for multi-objective particle swarm optimization

Author:

Liu JunORCID,Liu YanminORCID,Han Huayao,Zhang Xianzi,Shu Xiaoli,Chen Fei

Abstract

AbstractIn multi-objective particle swarm optimization, it is very important to select the personal best and the global best. These leaders are expected to effectively guide the population toward the true Pareto front. In this paper, we propose a two-stage maintenance and multi-strategy selection for multi-objective particle swarm optimization (TMMOPSO), which adaptively selects the global best and updates the personal best by means of hyper-cone domain and aggregation, respectively. This strategy enhances the global exploration and local exploitation abilities of the population. In addition, the excellent particles are perturbed and a two-stage maintenance strategy is used for the external archive. This strategy not only improves the quality of the solutions in the population but also accelerates the convergence speed of the population. In this paper, the proposed algorithm is compared with several multi-objective optimization algorithms on 29 benchmark problems. The experimental results show that TMMOPSO is effective and outperforms the comparison algorithms on most of the 29 benchmark problems.

Funder

Key Laboratory of Evolutionary Artificial Intelligence in Guizhou

Key Talens Program in digital economy of Guizhou Province

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Engineering (miscellaneous),Information Systems,Artificial Intelligence

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