Multi-Objective Particle Swarm Optimization with Comparison Scheme and New Pareto-Optimal Search Strategy

Abstract:

In the presented article, a novel multi-objective PSO algorithm, RP-MOPSO has been proposed. The algorithm adopts a new comparison scheme for position upgrading. The scheme is simple but effective in improve algorithms convergence speed. A sigma-density strategy of selecting the global best particle for each particle in swarm based on a new solutions density definition is designed. Experimental results on seven functions show that proposed algorithm show better convergence performance than other classical MOP algorithms. Meanwhile the proposed algorithm is more effective in maintaining the diversity of the solutions.

• Periodical: Applied Mechanics and Materials (Volumes 496-500)
• Pages: 1895-1900
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.496-500.1895
• Online since: January 2014
• Authors: Wen Wang, Wei Shen, Chao Long Ying, Xin Yi Yang
• Keywords: Multi-Objective Optimization (MOP), Particle Swarm Optimization (PSO), Relay Point, Sigma Method

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