中文摘要：

种群分割方法是混合蛙跳算法最重要的组成部分之一,直接影响算法的性能。针对多目标混合蛙跳算法,提出一种新的种群分割方法。该方法将代表潜在最优区域的非支配个体集合通过聚类的方式划分族群,目的是使不同族群在不同区域进行局部搜索,避免算法早熟。被支配个体则根据其与非支配个体集合的近似度分配到族群中,并通过随机加入其他族群个体的方式提高本族群的多样性。实验结果表明,相对于其他种群分割方法,本文的方法在提高多目标混合蛙跳算法的收敛性能和收敛速度方面都具有一定的优势,而且对于目标个数较多的问题也能获得较好的结果。

英文摘要：

Population partitioning strategy is one of crucial issues to the performance of shuffled frog leaping algorithm. This paper proposed a new population partitioning strategy in the ease of muhi-objeetive optimization. The idea is to divide the closely located non-dominated solutions into the same memeplexes so that different memeplexes evolve toward different potential optimal area of search space in order to prevent the optimizer from prematurity. The dominated individuals are assigned to memeplexes according to their approximation to the non-dominated set. Moreover, adding individuals from other memeplexes is used to promote the diversity of each memeplex. The comparative results with other partitioning methods on test problems involving up to ten objectives show that, the new method further improves the performance of muhi-objective shuffled frog leaping algorithm not only in convergence and speed of convergence to Pareto optimal front.