The Application And Research Of Chaotic Cuckoo Search

intelligent algorithm is one kind of biomimetic algorithm inspired by the mechanism of the natural organisms or natural phenomenon. Over the past ten years, with the rapid development of artificial intelligence, intelligent algorithms are emerging, and its applications are increasingly widespread.. CuckooSearch (CS), also known as cuckoo search, it is one of the intelligent algorithms, proposed in 2009 by Cambridge University Xin-SheYang and DEB Suash. CS is a new type of swarm intelligence algorithm, which effectively solving optimization problems by simulating certain species of cuckoo’s nest to find spawning behavior. Meanwhile, CS also uses associated Levy flight search mechanism, no more parameters to be set, strong search capabilities. CS algorithm in engineering optimization, objective function optimization, dynamic environment optimization, data mining and other fields are widely used. After the CS algorithm, arouse the interest of many researchers, and slowly developed into a research hotspot of intelligent algorithm. However, CS algorithm was only made in 2009 and research is still in its infancy, there are many shortcomings, such as easy to fall into local optimum, lack of search activity, and so on. Based on the above shortcomings, this paper introduced chaotic maps, proposed Chaotic Cuckoo Search, the main work is as follows:1. A cuckoo join chaos mapping algorithm is proposed. By chaotic maps, improved cuckoo algorithm relies entirely on random walk strategies, so that they do not have a fast convergence speed and with the increase of iteration steps, search activity and other shortcomings. In this algorithm, chaotic maps improve the cuckoo population diversity, making it difficult to get into a local optimum. By some classical function tests showed that not only improves the precision of its search algorithm, and accelerated the cuckoo convergence speed.2. Using the CCS algorithm to solve traveling salesman (TSP) problems and validate the algorithm. The simulation results show that the algorithm for solving TSP works well not only avoids the flaws of the smart algorithm is easily trapped in local minima, and increases the capability of global search for optimal solutions.3. The CCS algorithm to solve a double numerical integration based on the double integral shape of the integrand in two different directions randomly generate a number ranging from-node. Using CCS with optimization capabilities as an optimization algorithm to optimize these ranged from the node, optimized node can reflect to a large extent the shape characteristics of the integrand. The nodes as a division point on a double numerical integration algorithm. Simulation results show that this algorithm is simple and easy to implement, fast convergence, get the integral value of high precision, is a valid method of solving numerical integration.4.The text of the CCS algorithm combined with k-means algorithm, CCS-K-means algorithm is presented, for clustering of gene expression data analysis, experimental results show that the CCS-K-means algorithm for better results than the original k-means algorithm for clustering, and more stable.