Research And Application Of Bat Algorithm Based On Lévy Flight And Simplex Method

Bat algorithm is a swarm intelligence optimization algorithm, and the principle of this algorithm is to simulate the behavior of echolocation to find the optimum solution. With the characteristics of a simple structure, less parameters and robustness, bat algorithm was solved a lot of optimization problems. But there are some shortcomings of the basic bat algorithm that easy to fall into local optimum and has slow convergence in later phase of iterator. Therefore, bat algorithm was greatly limited in the range of its application. Clustering is the process of divide data objects into clusters or classes, the objects in same cluster have high similarity, but dissimilar in different clusters. General clustering methods, such as K-means clustering analysis method depends strongly on the initial solution in solving the clustering problem and easy to fall into local optimal solution, so that the accuracy of the solution is not high. Graph coloring problem is a classic NP-complete problem, does not exist an effective method can effectively solve it completely. In this thesis, some problems exist in bats algorithms are analyzed. Aim to broaden the theory and improve the performance of the bat algorithm, then expand its scope of application. Some advantages of other swarm intelligence algorithm are combined into bat algorithm to improve the performance of bat algorithm.Combine with the echolocation biological properties of bats, based on the algorithm framework of bat algorithm. The "global search" operator of bat algorithm using Lévy flights to implements. Meanwhile, in the "global search" phase also incorporates a mechanism that the exchange of information between individuals of the population. A Isomeric bat algorithm based on Lévy flight and the Simplex method was proposed. The simplex algorithm method was used to guide "local search" of bat algorithm. The improved algorithm can effectively accelerate the convergence rate and improve the accuracy of solution of the algorithm, and also successfully avoid premature, so that the performance of the bat algorithm has been improved. Using the continuous Isomeric bat algorithm to solve the clustering problem, provide a reference for solving the clustering problem. The global search and local search of Isomeric bat algorithm are discretized. Combined with the characteristics of graph coloring problem, the local exchange and local sequence inversion operation are introduced to deal with the conflict nodes, and to solve plan coloring problems using discrete Isomeric bat algorithm. Results of the simulation indicated the proposed algorithm is effective and feasible. Compared with other optimization algorithms, the Isomeric bat algorithm have demonstrated a more excellent performance in convergence speed, solution accuracy and robustness.