Improvement And Application Research Of Cuckoo Search Algorithm Based On Swarm Optimization Strategy

Cuckoo search algorithm is proposed by Xin-She Yang and Deb Suash, the professors of the Cambridge University in 2009. This algorithm is inspired by cuckoo parasitism reproductive behavior and have become a new heuristic optimization algorithm. Compared with other heuristic optimization algorithms, cuckoo search algorithm Simple, easy to operate, less parameters, easy to understand and implement, etc. Therefore, more and more attention has been paid by the domestic and foreign scholars. Cuckoo search algorithm has gradually become one of the hot spots in the field of computational intelligence research. In recent years, cuckoo search algorithm has been successfully applied to solving complex combinatorial optimization problems. But, with the deepening of research, people found cuckoo search algorithm still has some shortcomings, such as easy to fall into the local optimum and the low optimization accuracy.In this paper, we aim at cuckoo search algorithm and its existing problems, from the model structure of the algorithm and evolution strategies to improve cuckoo search algorithm and some improved versions of the cuckoo search algorithm are proposed. The purpose is to improve the cuckoo search algorithm optimization performance, and improve the research on the theory of cuckoo search algorithm.The main research achievements obtained are as following:(1) An idea of master-slave model structure are introduced into cuckoo search algorithm, proposed a master-slave structure cuckoo search algorithm. Inspired by the master-slave structure mode of employers/workers, from the structure improved cuckoo search algorithm. Simulation results show that the improved cuckoo search algorithm accuracy and convergence speed is improved obviously.(2) Cuckoo search algorithm is easy to fall into premature convergence and low accuracy. In order to overcome these shortcomings, a cuckoo search algorithm with elite opposition-based strategy is proposed. The algorithm uses the elite reverse strategy, enhances the diversity of the population, improve the algorithm’s global searching ability and avoid fall into local optimum, overcomes the disadvantages of optimization precision.(3) In view of cuckoo search algorithm poor population diversity in the late, weak local search ability and slow convergence speed, an improved cuckoo search algorithm using chaotic local search and simplex method is proposed. The Simulation results show that the improved cuckoo search algorithm can effectively increase the diversity of population, improve searching precision and convergence speed.