计算机科学 ›› 2020, Vol. 47 ›› Issue (12): 218-225.doi: 10.11896/jsjkx.191100207
霍林, 郭雅蓉, 覃志健
HUO Lin GUO, Ya-rong, QIN Zhi-jian
摘要: 针对乌鸦算法收敛速度慢、容易陷入局部最优的问题提出了一种具有自适应步长的柯西变异乌鸦算法(Cauchy mutation crow search algorithm with adaptive step sizeCMCSA)对标准乌鸦算法中两种情况下的位置更新策略进行了改进.在每次迭代时利用柯西变异优化gbest来增强全局搜索能力和增大变异范围以提高种群多样性避免陷入局部最优;引入判别概率在引导者发现自己被跟随的情况下优化当前个体的位置更新策略;根据当前位置和引导者之间的位置距离自适应地调整步长使算法平稳快速地收敛到全局最优从而控制搜索速度和精度有效弥补了标准CSA寻优方式的盲目性和收敛速度慢的缺陷.为评价CMCSA算法的有效性将其应用于10个基本测试函数进行寻优实验并与其他8种智能优化算法进行比较.实验结果表明所提算法的平均收敛性和鲁棒性都优于其他算法寻优平均值和标准差的平均排名均为第一总体性能良好.
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