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提出了一种基于置换策略的多目标差分进化算法,采用置换策略来实现优化空间由连续向离散的转换,并结合Pareto快速分层排序策略和基于聚集密度的按层修剪操作对种群进行更新维护,以保持解集的逼近性和分布性。以多目标0/1背包问题为例进行实验,结果表明该算法能有效求解离散型多目标优化问题,优于经典的NSGA-II算法。
A multi-objective differential evolution algorithm based on permutation strategy is proposed. The permutation strategy is adopted to realize the transformation of the optimization space from continuous to discrete, and the Pareto rapid stratification strategy and the layer-trim based on the density of aggregation are used to update and maintain the population , In order to maintain the approximation and distribution of solution set. Taking the multiobjective 0/1 knapsack problem as an example, the experimental results show that this algorithm can effectively solve the discrete multi-objective optimization problem, which is better than the classical NSGA-II algorithm.