An improved multi-objective particle swarm optimizer for multi-objective problems

• Published in: Expert systems with applications Vol. 37; no. 8; pp. 5872 - 5886
• Main Authors: Tsai, Shang-Jeng, Sun, Tsung-Ying, Liu, Chan-Cheng, Hsieh, Sheng-Ta, Wu, Wun-Ci, Chiu, Shih-Yuan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2010
• Subjects: Benchmarking; Cluster; Dealing; Disturbance; Disturbances; Expert systems; Exploration; Falling; Global best particle; Jump improved operation; Multi-objective optimization; Optimization; Particle swarm optimizer; Proportional distribution; Repositories; Global best particle; Proportional distribution; Jump improved operation; Disturbance; Cluster; Multi-objective optimization; Particle swarm optimizer
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2010.02.018

This paper proposes an improved multi-objective particle swarm optimizer with proportional distribution and jump improved operation, named PDJI-MOPSO, for dealing with multi-objective problems. PDJI-MOPSO maintains diversity of new found non-dominated solutions via proportional distribution, and combines advantages of wide-ranged exploration and extensive exploitations of PSO in the external repository with the jump improved operation to enhance the solution searching abilities of particles. Introduction of cluster and disturbance allows the proposed method to sift through representative non-dominated solutions from the external repository and prevent solutions from falling into local optimum. Experiments were conducted on eight common multi-objective benchmark problems. The results showed that the proposed method operates better in five performance metrics when solving these benchmark problems compared to three other related works.