基于参数自适应策略的改进乌鸦搜索算法

Improved Crow Search Algorithm Based on Parameter Adaptive Strategy

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。

Crow search algorithm(CSA)is a new intelligent optimization algorithm developed in recent years.It has the advantages of high optimization accuracy and fast convergence speed.However,its search performance is strongly dependent on its parameters.The selection of parameters is very important to the global search ability as well as the convergence speed of the algorithm.In order to solve the problem of determining the optimal parameters,a method for characterizing the convergence process of the population optimization algorithm is proposed first,so that the optimization process can be divided into pre-,mid-,and late stages.On this basis,an adaptive parameter improved Crow search algorithm(APICSA)based on the optimization process is proposed.The test results of Levy No.5 function and gear system design problem show that the reliability and convergence speed of APICSA method can be better balanced,and both are improved to a certain extent.Compared with other intelligent optimization algorithms such as artificial bee colony algorithm(ABC),the standard deviation of this method in 50 operations is reduced by 55%,and the error between the average value and the optimal solution is reduced by 67.7%,which show that APICSA algorithm performs better in reliability and accuracy.