Research On The Artifical Bee Colony Algorithms And Applications

In recent years, with the great potential of the data processing and analysis of the swarm intelligence algorithm, a large number of researchers have devoted themselves to the research. But there are many problems in this kind of swarm intelligence algorithm, which is not the optimal solution and the convergence rate of these methods, which limits the practical application of these methods. There are still a lot of ways to go, which also provides a practical significance for our project.Inspired by the nature and the efforts of researchers, the artificial bee colony algorithm is proposed. Along with the research, the artificial bee colony algorithm has made great success in dealing with the problem of clustering classification. Past research mainly focused on the performance improvement, function optimization, combinatorial optimization, convergence of these aspects.All along, the integration of different ideas and the combination of subject is the important source of innovation, which is also the trend of the artificial bee colony algorithm can further develop and apply. The main artificial intelligence, such as machine learning, statistics and databases, and other disciplines, is now a hot research topic.In this paper, we have proposed the algorithm, in this paper, we have proposed the algorithm, and the algorithm is improved by experiments. Then, the algorithm is improved. The algorithm is improved. Then, by comparing the algorithm. The algorithm is improved. The algorithm is improved. Then, we propose the algorithm. Finally, we propose the algorithm. Finally, we propose that the improved strategy. In theory, these limitations are solved. The specific contributions of this dissertation are as follows:(1) the theory of bee colony algorithm and its application to specific research are described in detail. We elaborate on the mathematical model of the original artificial bee colony algorithm, we apply this model to the common problems in our research, and it can solve these problems perfectly.(2) improved algorithm of artificial bee colony algorithm. In the third chapter, we have exposed some shortcomings of the bee colony algorithm. We propose 4 improved strategies for the improvement of the initial solution, the improvement of the selection strategy, the improvement of the algorithm, and the improvement of the algorithm. And through the simulation experiment to verify the effectiveness of these update strategies.(3) artificial bee colony algorithm for variable group size. This is the problem of the convergence and accuracy of the bee colony algorithm. The new algorithm framework is proposed. The population size of the swarm is expanded by multiple iterations of the swarm.