Multiobjective Optimization Algorithm Based on Evolutionary Strategy

While using evolutionary strategy to solve multiobjective optimization,in order to improve exploration of the solutions in decision space and maintain the diversity of the pareto front,a multiobjective optimization algorithm based on evolutionary strategy is presented.The evolutionary strategy of self-adaptive mutation step is used to search solutions in the globle area and local area.And the non-dominated solution in certain ratio enters the next generation,so the dominated individual has opportunity to participate multiplying in the next generation,and the diversity of the pareto front is assured.The simulation results show the good performance of the proposed algorithm.