A Particle Swarm Optimization with Feasibility-based Rules for Mixed-variable Optimization Problems

摘要：

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.

关键词： Particle Swarm Optimization Feasibility-based rules Mixed-variables

作者: Chao-Li Sun Jian-Chao Zeng Jeng-Shyang Pan

作者单位: Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technolo Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, Guangdong, 518055 Department of

会议类型: 国际会议

会议名称: 2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

会议地点: 沈阳

会议语种:英文

页码: 1-5

在线出版日期: 2009-08-12（万方平台首次上网日期，不代表论文的发表时间）