A Particle Swarm Optimization with Feasibility-Based Rules for Mixed-Variable Optimization Problems

A Particle Swarm Optimization algorithm with feasibility-based rules (FRPSO) is proposed in this paper to solve mixed-variable optimization problems. An approach to handle various kinds of variables is discussed. Constraint handling is based on simple feasibility-based rules, not needing addinional penalty parameters and not guaranteeing to be in the feasible region at all times. Two real-world mixed-varible optimization benchmark problems are presented to evaluate the performance of the FRPSO algorithm, and it is found to be highly competitive compared to other existing stochastic algorithms. Keywords- Particle Swarm Optimization; Feasibility-based rules; Mixed-variables

• Type: Journal
• Year: 2009
• Where: HIS
• Authors: Chao-Li Sun, Jian-Chao Zeng, Jeng-Shyang Pan
• Keywords: Feasibility-based Rules, HIS 2009, Information Technology, Particle Swarm Optimization, Swarm Optimization Algorithm