A multi-objective particle swarm optimization algorithm based on two-archive mechanism

• Published in: Applied soft computing Vol. 119; p. 108532
• Main Authors: Cui, Yingying, Meng, Xi, Qiao, Junfei
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2022
• Subjects: Evolutionary information; Genetic operator; Multi-objective particle swarm optimization; Two-archive mechanism; Multi-objective particle swarm optimization; Evolutionary information; Genetic operator; Two-archive mechanism
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2022.108532

As a powerful optimization technique, multi-objective particle swarm optimization algorithms have been widely used in various fields. However, performing well in terms of convergence and diversity simultaneously is still a challenging task for most existing algorithms. In this paper, a multi-objective particle swarm optimization algorithm based on two-archive mechanism (MOPSO_TA) is proposed for the above challenge. First, two archives, including convergence archive (CA) and diversity archive (DA) are designed to emphasize convergence and diversity separately. On one hand, particles are updated by indicator-based scheme to provide selection pressure toward the optimal direction in CA. On the other hand, shift-based density estimation and similarity measure are adopted to preserve diverse candidate solutions in DA. Second, the genetic operators are conducted on particles from CA and DA to further enhance the quality of solutions as global leaders. Then the search ability of MOPSO_TA can be improved by performing hybrid operators. Furthermore, to balance global exploration and local exploitation of MOPSO_TA, a flight parameters adjustment mechanism is developed based on the evolutionary information. Finally, the proposed algorithm is compared experimentally with several representative multi-objective optimization algorithms on 21 benchmark functions. The experimental results demonstrate the competitiveness and effectiveness of the proposed method. •The two-archive mechanism is incorporated into multi-objective particle swarm optimization algorithm.•The global leader is selected from candidate solutions generated by performing genetic operators between CA and DA.•An improved self-adaptive flight parameters strategy is utilized to balance exploitation and exploration of MOPSO_TA.•The experimental results verify the competitiveness and effectiveness of the proposed MOPSO_TA.