Shi Jiao, Gong Maoguo, Ma Wenping, Jiao Licheng
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an 710071, China.
ScientificWorldJournal. 2014 Feb 12;2014:539128. doi: 10.1155/2014/539128. eCollection 2014.
How to maintain the population diversity is an important issue in designing a multiobjective evolutionary algorithm. This paper presents an enhanced nondominated neighbor-based immune algorithm in which a multipopulation coevolutionary strategy is introduced for improving the population diversity. In the proposed algorithm, subpopulations evolve independently; thus the unique characteristics of each subpopulation can be effectively maintained, and the diversity of the entire population is effectively increased. Besides, the dynamic information of multiple subpopulations is obtained with the help of the designed cooperation operator which reflects a mutually beneficial relationship among subpopulations. Subpopulations gain the opportunity to exchange information, thereby expanding the search range of the entire population. Subpopulations make use of the reference experience from each other, thereby improving the efficiency of evolutionary search. Compared with several state-of-the-art multiobjective evolutionary algorithms on well-known and frequently used multiobjective and many-objective problems, the proposed algorithm achieves comparable results in terms of convergence, diversity metrics, and running time on most test problems.
如何保持种群多样性是设计多目标进化算法中的一个重要问题。本文提出了一种增强的基于非支配邻域的免疫算法,其中引入了多群体协同进化策略以提高种群多样性。在所提出的算法中,子群体独立进化;因此每个子群体的独特特征能够得到有效保持,并且整个种群的多样性也能有效增加。此外,借助设计的协作算子获取多个子群体的动态信息,该算子反映了子群体之间的互利关系。子群体有机会交换信息,从而扩大了整个种群的搜索范围。子群体相互利用参考经验,从而提高了进化搜索的效率。与一些在著名且常用的多目标和多目标问题上的最先进多目标进化算法相比,所提出的算法在大多数测试问题的收敛性、多样性指标和运行时间方面取得了可比的结果。