中文摘要：

设计了多目标混沌进化算法（MCEA），在每一代遗传操作和外部档案调整完成之后，该算法从外部档案中随机选择部分个体，对这些个体的拷贝进行混沌搜索，以产生更多非劣解．将强度Pareto进化算法（SPEA）和SPEA2分别与基于Logistic映射的混沌搜索结合而产生的MCEAs应用于一些复杂多目标优化问题，计算结果表明，混沌的加入，明显改善了多目标进化算法（MOEA）各方面的性能．

英文摘要：

Multi-objective chaotic evolutionary algorithm （MCEA） is designed. In each generation of MCEA, after the population finishes all genetic operations and external archive maintenance is done, chaotic search is performed on the copy of several individuals randomly chosen from the external archive to obtain new non-dominated solutions. MCEAs respectively merging strength Pareto evolutionary algorithm （ SPEA）, SPEA2 with chaotic search based on Logistic map are applied to some complex multi-objective optimization problems. The computational results demonstrate that the comprehensive performance of multi-objective evolutionary algorithm （MOEA） is improved as a consequence of the inclusion of chaos.