Multi objective particle swarm optimization using enhanced dominance and guide selection

Nowadays, the core of the Particle Swarm Optimization (PSO) algorithm has proved to be reliable. However, faced with multi-objective problems, adaptations are needed. Deeper researches must be conducted on its key steps, such as solution set management and guide selection, in order to improve its efficiency in this context. Indeed, numerous parameters and implementation strategies can impact on the optimization performance in a particle swarm optimizer. In this paper, our recent works on those topics are presented. We introduce an ε dominance variation which enables a finer neighborhood handling in criterion space. Then we propose some ideas concerning the guide selection and memorization for each particle. These methods are compared against a standard MOPSO implementation on benchmark problems and against an evolutionary approach (NSGAII) for a real world problem: SVM classifier optimization (or model selection) for a handwritten digits/outliers discrimination problem.