Intelligent multiobjective particle swarm optimization based on AER model


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.