计算机科学 ›› 2017, Vol. 44 ›› Issue (3): 259-263.doi: 10.11896/j.issn.1002-137X.2017.03.053
魏政磊,赵辉,韩邦杰,孙楚,李牧东
WEI Zheng-lei, ZHAO Hui, HAN Bang-jie, SUN Chu and LI Mu-dong
摘要： 灰狼优化算法(Grey Wolf Optimization,GWO)是一种新型的群智能优化算法。与其他智能优化算法类似,该算法仍存在收敛速度慢、容易陷入局部极小点的缺点。针对这一问题,提出了具有自适应搜索策略的改进算法。为了提高算法的收敛速度和优化精度,通过适应度值控制智能个体位置,并引入了最优引导搜索方程；另一方面,为提高GWO的种群多样性,改进算法利用位置矢量差随机跳出局部最优。最后对10个标准测试函数进行了仿真实验,并与其他4种算法进行了比较,统计结果和Wilcoxon符号秩检验结果均表明,所提出的改进算法在收敛速度以及搜索精度方面具有明显优势。
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