A Ε-domination Based Multi-objective Particle Swarm Optimization

A multi-objective particle swarm optimization algorithm based on Ε-dominance is proposed. The Ε-dominance is applied to update the external set in order to obtain the Pareto set with better distribution, and the dynamic adjustment strategy, which made the algorithm achieving the search and approximation to the Pareto set, is adopted in the iterative process of the Ε-Pareto solution set. Three benchmark cases were tested and the results show that this algorithm is much more efficient than the DNPSO.