计算机科学 ›› 2013, Vol. 40 ›› Issue (10): 235-238.
谢承旺,李凯,廖国勇
XIE Cheng-wang,LI Kai and LIAO Guo-yong
摘要: 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。
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