Abstract: A CoMaCOA(Co-evolution Multi-Objective Constrained) optimization algorithm is proposed to deal with the problem that it cannot be combined convergence and diversity effectively in multi-objective COA (Constrained Optimization Algorithms). First, a COA is transformed into the multi-objective evolutionary algorithm with dynamic constraint processing. Then, DE(Differential Evolution) is used to generate the first population. The second population is generated by the known feasible solution in the first population and coevolved with the first. The first population accelerates convergence by global search that does not deal with constraints. The second population evolves through local search to maintain and obtain more feasible solutions. Finally, the standard constrained multi-objective test function is used for experiments in order to test the performance of the proposed algorithm. The experiment result shows that the proposed algorithm achieves good results on both IGD( Inverted Generational Distance) and HV( Hypervolume), comparing with PF ( Penalty Function) method and dynamic boundary processing to constrain problem DCMaOP(Dynamic Constrained Many Objective optimization Problem). It shows that the algorithm is both effective in convergence and diversity.

Key words: multi-objective evolutionary algorithm')">

multi-objective evolutionary algorithm, dynamic constraint processing, co-evolution, global search