Abstract: 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.

Key words: Adaptive strategy, Crow search algorithm, Engineering design, Optimization