An evolutionary particle swarm algorithm for multi-objective optimisation
Minyou Chen, Chuansheng Wu, Peter John Fleming · 2008
An Evolutionary Particle Swarm Optimisation (EPSO) approach is presented to improve the performance of PSO algorithm for multi-objective optimisation. The proposed approach incorporates non-dominated sorting, adaptive inertia weight and a special mutation operation into particle swarm optimisation to enhance the exploratory capability of the algorithm and improve the diversity of the Pareto solutions. To deal with multi-objective optimisation problems, we use dominance-based rank to guide the flight of particles. The proposed algorithm has been validated using several well-known benchmark test functions and successfully applied to the multi-objective optimal design of alloy steels, which aims at determining the optimal process parameters and the required weight percentages of the chemical composites in order to obtain the pre-defin1ed mechanical properties of the materials. The results have shown that the algorithm can locate the constrained optimal design with a very good accuracy.