Intelligent particle swarm optimization in multiobjective optimization
Xiaohua Zhang, Meng Hong-yun, Jiao Li-cheng · 2005
How to find a sufficient number of uniformly distributed and representative Pareto optimal solutions is very important for multiobjective optimization (MO) problems. A new model for particle swarm optimization is constructed firstly, and then an intelligent particle swarm optimization (IPSO) for MO problems is proposed based on AER (agent-environment-rules) model, in which competition operator and clonal selection operator are designed to provide an appropriate selection pressure to propel the swarm population towards the Pareto-optimal front. The quantitative and qualitative comparisons indicate that the proposed approach is highly competitive and that can be considered as a viable alternative to solve MO problems.