计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 260-263.doi: 10.11896/jsjkx.201100158
林忠甫, 颜力, 黄伟, 李洁
LIN Zhong-fu, YAN Li, HUANG Wei, LI Jie
摘要： 乌鸦搜索算法(CSA)是近年发展起来的一种新型智能优化算法,具有搜索精度高、收敛速度快等优点,但是其搜索性能对参数依赖性较强,参数的选取对算法的全局搜索能力、收敛速度至关重要。为解决最佳参数的确定问题,首先提出了一种用于表征种群优化算法收敛进程的方法,从而将优化过程分为前、中、后期,并在此基础上提出了一种基于优化过程的自适应参数乌鸦搜索算法(APICSA)。经Levy No.5函数和齿轮系统设计问题对APICSA算法的测试表明,相对于标准CSA算法,该方法的可靠性和收敛速度可以得到更好的平衡,且均有一定程度的提高。与人工蜂群算法(ABC)等其他智能优化算法相比,该方法在50次运算中的标准差比ABC算法减小了55%,平均值与最优解的误差减小了67.7%,说明APICSA算法在可靠性和精度上具有更大优势。
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