In order to improve the convergence and diversity performance, a local search and hybrid diversity strategy
based multi-objective particle swarm optimization algorithm(LH-MOPSO) is proposed. LH-MOPSO makes full use of the
augmented Lagrange multiplier method to approach the Pareto optimal solutions quickly, and the hybrid diversity strategy
based on modified Maximin fitness function and crowding distance is used for maintaining the diversity of nondominated
solutions. Meanwhile, Gaussian mutation operator is introduced to avoid LH-MOPSO premature convergence. Finally, an
efficient constraint handling method is proposed. Simulation results show that LH-MOPSO has good performance.