摘要
介绍递归式多目标遗传算法(Recursive Multiple Objective Genetic Algorithms,RMOGA).整个进化过程分为与目标数量相等的几个递归阶段,每个阶段多增入一个目标.每个阶段先用一个独立的群体进化新增的目标;该群体中性能较好的个体与上一阶段多目标进化的群体联合形成已增目标集的初始群体.实验结果表明,在绝大多数问题中,RMOGA的性能优于NSGA-Ⅱ,SPEA及PAES等3个典型的多目标遗传算法.
Recursive Multiple Objective Genetic Algorithms（RMOGA） is proposed. The whole evolution process is divided into the same recursive phases as the numbers of objectives and one more objective is added in each phase. In each phase, a new objective is evolved on an independent population. Then the better individuals selected from the new objective population together with the multi-objective population evolved in the last phase generate the initial population for the added objective set to evolve. The experiment results show that RMOGA has better performance than that of three other typical multiple objective genetic algorithms： NSGA-Ⅱ, SPEA and PAES in most problems.
出处
《上海海事大学学报》
北大核心
2007年第2期62-66,74,共6页
Journal of Shanghai Maritime University
基金
上海市重点学科建设项目(T0602)
上海海事大学重点学科建设项目(XL0101-1)
关键词
递归式问题解决
多目标优化
多目标遗传算法
多目标问题
recursive problem solving
muhi-objective optimization
multi-objective genetic algorithm
multiple objective problem
