The Improving Research On Artificial Fish Swarm Algorithm And Cuckoo Search Algorithm

Swarm intelligence optimization algorithm has been widely applied in the aspects of engineering and management in present, and it has become an important research direction in the domain of information. So it has an important theoretical research value and practical value that this paper carried out the improved study on the artificial fish swarm algorithm(AFSA) and the cuckoo search algorithm(CS). The work of this paper can be summarized in the following three parts:First of all, this paper has proposed an improved AFSA called a fish swarm algorithm by using dynamic swimming-modes. In the proposed algorithm, swimming mode of artificial fish is reconstructed which make the group information to get a better transmission. What’s more, the search efficiency of individuals is improved and the ability of the algorithm to avoid falling into local optimal is enhanced. And then both the global search ability and optimization performance of the algorithm are improved.Secondly, aimimg to overcome the shortcomings of the basic cuckoo search algorithm such as: weak global search ability, easy to fall into local optimum, this paper proposed a cuckoo search algorithm with complex method(CMCS). Based on CS and complex method, complex method is applied to the cuckoo egg characteristics to enhance the competitiveness of populations, so as to improve the search efficiency and optimization performance of the algorithm.Thirdly, in order to overcome the shortcomings of basic CS when solving complex high dimensional optimization problems, we present a novel cloud model cuckoo search algorithm(CCS). This algorithm improves the search ability of the algorithm, and shows a better optimization performance when applied to solve complex high dimensional optimization problems.