Multi-Objective Particle Swarm Optimization Algorithm Based on Enhanced ε-Dominance

In this paper, we describe a multi-objective particle swarm optimization algorithm (MOPSO) that incorporates the concept of the enhanced epsiv-dominance. We present this new concept to update the archive. The archiving technique can help us to maintain a sequence of well-spread solutions. A new particle update strategy and the mutation operator are shown to speed up convergence. To compare with the state-of-art MOEAs on a well-established suite of test problems, our new approach is simple constructed, and results indicate that it works effectively and has steady-state performance. It is confirmed from the results that the proposed method outperforms other methods