中文摘要：

针对多目标遗传算法存在的局部搜索能力弱和易早熟的问题，采取理论分析方法，提出了一种新的基于混沌局部搜索的多目标遗传算法（MOGA-CLS）。对按支配关系形成的第1、2层Pareto解进行混沌搜索获得更优解，并采用基于聚集度的聚类方法保持种群和Pareto解集的多样性和分布性。仿真实验结果表明：该算法能有效地提高搜索效率。

英文摘要：

In view of overcome the deficiencies of the multi-objective genetic algorithm, such as poor local search capability and premature convergence, a new multi-objective genetic algorithm （MOGA-CLS） was developed based on chaotic local search. A better solution was obtained around the 1st and 2nd rank of Pareto solutions with MOGA-CLS. Also, the distribution and diversity of population and Pareto solution set were maintained by adopting the cluster method based on crowding degree. The simulation results show that the new algorithm MOGA-CLS can greatly improve the searching efficiency.