Particle swarm with equilibrium strategy of selection for multi-objective optimization

A new ranking scheme based on equilibrium strategy of selection is proposed for multi-objective particle swarm optimization (MOPSO), and the preference ordering is used to identify the “best compromise” in the ranking stage. This scheme increases the selective pressure, especially when the number of objectives is very large. The proposed algorithm has been compared with other multi-objective evolutionary algorithms (MOEAs). The experimental results indicate that our algorithm produces better convergence performance.