Computer Science ›› 2013, Vol. 40 ›› Issue (10): 235-238.

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Improved NSGA2Algorithm with Differential Evolution Local Search

XIE Cheng-wang,LI Kai and LIAO Guo-yong   

  • Online:2018-11-16 Published:2018-11-16

Abstract: NSGA2algorithm with its selection mode of Pareto dominate method and the strategy of using individual density estimation operator of solution to select winning solution becomes the model of modern multi-objective evolutionary algorithm,but the algorithm by computing the solution of individual crowding distance to keep the population distribution mechanisms has certain defects.In view of this,this paper proposed a kind of improved algorithm which takes differential local search with NSGA2algorithm.The new algorithm uses the differential evolution mutation operator in directional guiding ideology,takes the difference vector,and combines the NSGA2algorithm to improve the solution population distribution.Simulation results show that the new algorithm compared with the NSGA2algorithm in the solution of cluster distribution uniformity and depth is improved obviously.In addition,the new algorithm in the time complexity is same as the classic NSGA2algorithm.

Key words: Differential evolution,Local search,NSGA2,Diversity

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