Study of Hybrid Multi-objective Particle Swarm Optimization Based on Hill Climbing Strategy with Sidesteps
To improve the convergence and diversity performance of multi-objective particle swarm optimization(MOPSO),a hybrid MOPSO(H-MOPSO) based on hill climbing strategy with sidesteps is proposed.The hybrid model of local search and particle swarm optimization is firstly established.According to the model,the local search algorithm based on hill climbing strategy with sidesteps is introduced periodically in late period with optimized parameters,making particles search along descent direction while they are away from Pareto front,and search along the front while they are near Pareto front.The non-uniform mutation operator is used and the inertia weight is set to be linearly decreased to avoid premature.Simulation results indicate that the proposed algorithm has favorable performance comparing to MOPSO,NSGA-II and MOEA/D.