Cuckoo Search Based On Frog Leaping Local Search And Chaos Theory

In the late 20 th century, with the rapid development of science and technology, intelligent heuristic algorithm stand out, by simulating the biological behavior or natural phenomena to solve the problem, has gradually become a hot topic in the field of the whole optimization. Moreover, many intelligence optimization algorithms have been successfully applied in the field engineering optimization, and the basic theory is demonstrated.In 2009, Cambridge University Yang Xin-she and Raman Engineering University of DEB Suash through simulation cuckoo find nest spawning behavior, put forward a new swarm intelligence optimization algorithm,(cuckoo, cuckoo search, CS) algorithm. Compared with the traditional algorithm, the CS algorithm has the characteristics of less control parameters, excellent search path, good robustness, strong global search ability and so on. Because of the weak local search ability, slow search speed and low convergence precision of CS algorithm, this paper improves the CS algorithm to solve this problem. The main work is as follows:(1) First of all, analyze the cuckoo(CS) algorithm research, introducing the background of the cuckoo algorithm, basic knowledge of relevant theories, including the cuckoo algorithm principle, steps, and the current research status(2) Put forward an improved algorithm of the cuckoo. Firstly, using the chaos theory to add the variety of the initial population, in order to improve the algorithm of global optimization ability. Secondly, we introduce the shuffled frog leaping algorithm as the local search mechanism to strengthen the local search in order to improve the convergence speed of the algorithm. Finally, introduce the inertia weight, the cuckoo algorithm has a tendency to expand the search space, and have the ability to search for new area.(3) On the theory of stochastic process and Markov model improve the theoretical proof of the cuckoo algorithm, which proves the convergence of the improved cuckoo algorithm. At the same time, through a complex benchmark testing functions improve the cuckoo search algorithm of simulation experiments; the effectiveness and feasibility of the algorithm are verified. It is shown that improved the cuckoo search algorithm has strong stability and global search ability.