A Cuckoo Search Algorithm based on Self-adjustment Strategy

Abstract

Cuckoo search algorithm(CS) has been successfully applied to optimization problem. However, CS has the obvious phenomenon of the premature convergence problem and is easily trapped into local optimum. In order to overcome the shortcomings and further improve the performance of CS, A cuckoo search algorithm based on self-adjustment strategy(SACS) is proposed. Eight benchmark test functions are selected to compare with other improved CS algorithm. The simulation results show that the SACS better convergence rate and optimization accuracy than CS, ICS and ASCS when dealing with high-dimensional function optimization problems.