混合策略改进的乌鸦搜索算法.

In order to improve the CSA with low speed of convergence and insufficient convergence accuracy. This paper proposed a MSCSA. Firstly, the algorithm introduced an adaptive weight coefficient with disturbance of tent sequence at early stage which could improve the convergence speed of the algorithm. Secondly, the algorithm introduced an operator which mixed by golden sine and moth-flame at late stage. At last, the algorithm improved the discovery probability AP which could improve the convergence accuracy of the algorithm by increasing the randomness. This paper determined the value of the iteration coefficient by comparing and testing on 9 benchmark functions. The paper verified the performance of the algorithm by the Wilcoxon rank sum test. The results of the test prove that the performance of MSCSA is better. [ABSTRACT FROM AUTHOR]