关键词: 多目标优化, 离散变量, 遗传算法, 就近取值, 离散交叉算子, 离散变异算子

Abstract: In this paper，in view of insurfficient precision and reliability of existing approaches to solve multi－objective optimization problems which include discrete variables，a new strategy called nearest valued based on characteristics both of multi－objective genetic algorithms and discrete optimization problems is proposed. Traditional strategy transforms discrete multi－objective optimization problems into continue multi－objective optimization problems and then takes advantage of strategies that deal with continue variables to optimize it. Furthermore，we have achieved optimal solution set of corresponding continue optimization problems. Finally，some special discretization methods are utilized to handle the problems and achieve discrete optimal solution set is reaplaced by nearest valued strategy. So，we get a DIS－NSGA－Ⅱ(dispersed non－dominate sort genetic algorithmⅡ)which is suitable for discrete multi－objective optimization problems from applying nearest valued strategy in geneic operators of traditional multi－objective optimization algorithm NSGA－Ⅱ(non－dominate sort genetic algorithmⅡ)and gain a discrete crossover operator and a mutation operator. The biggest advantage of DIS－NSGA－Ⅱ is that it can make optimization algorithm authentically optimizing in discrete decision variable space. In theory，nearest valued strategy has greater advantages that its processing strategy is more reasonale. In the end，the effectiveness of the algorithm is verified by comparing two kinds of typical algorithms and the data shows that the algorithm has greater advantages in dealing with discrete multi－pbjective optimization problems.

Key words: multi－objective optimization, discrete variables, genetic algorithm, nearest valued strategy, discrete crossover operator, discrete mutation operator