Intelligent multiobjective particle swarm optimization based on AER model

Authors:
Hong-yun Meng;Xiao-hua Zhang;San-yang Liu
Affiliations:
Dept. of Applied Math., XiDian University, Xian, China;Institute of Intelligent Information Processing, XiDian University, Xian, China;Dept. of Applied Math., XiDian University, Xian, China
Venue:
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
Year:
2005

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Abstract

How to find a sufficient number of uniformly distributed and representative Pareto optimal solutions is very important for Multiobjective Optimization (MO) problems. An Intelligent Particle Swarm Optimization (IPSO) for MO problems is proposed based on AER (Agent-Environment-Rules) model, in which competition and clonal selection operator are designed to provide an appropriate selection pressure to propel the swarm population towards the Pareto-optimal Front. An improved measure for uniformity is carried out to the approximation of the Pareto-optimal set. Simulations and comparison with NSGA-II and MOPSO indicate that IPSO is highly competitive.