一种带差分局部搜索的改进型NSGA2算法

Improved NSGA2 Algorithm with Differential Evolution Local Search

NSGA2算法以其Pareto支配的选择模式并辅以解个体密度估计算子选择胜出解的策略而成为了现代多目标进化算法的典范,但是该算法通过计算解个体的聚集距离来保持群体的分布性的机制存在一定的缺陷。鉴于此,提出了一种带差分局部搜索的改进型NSGA2算法。新算法利用差分进化中变异算子的定向引导作用,抽取其中的差分向量,并与NSGA2算法结合以改善解群的分布性。仿真实验表明:新算法较NSGA2算法在解群分布的均匀性和广度上有明显的改善。此外,新算法在时间复杂性方面与经典的NSGA2算法相当。

NSOA2 algorithm 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 evolu-tionary 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 NSOA2 algorithm. The new algorithm uses the differential evolution mutation opera-tor in directional guiding ideology, takes the difference vector, and combines the NSGA2 algorithm to improve the solu-tion population distribution. Simulation results show that the new algorithm compared with the NSGA2 algorithm 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 NSGA2 algorithm.