A new hybrid optimizer is proposed in which an innovative local optimal particles search strategy, which on basis of particular analysis on disadvantage of global optimal particle method, is integrated into multi-objective particle swarm optimization.

Select some non-dominated solutions lied in less-crowded region of external archive and make full use of optimal particles method to guide these solutions approach the Pareto front quickly.

The multi-dimensional uniform mutation operator is performed to prevent algorithm from trapping into local optimum and a dynamic archive maintenance strategy is applied to improve the diversity of solutions.

For coping with the constrained conditions consist in objective functions, we adopt an efficient infeasibility degree evaluation criterion to deal with these complex problems.