The Mixture Of Tabu Search Bats Research Bats Research And Application Of The Algorithm

The support vector machine (SVM) was proposed by Vapnik et al. In 1995. Support vector machine is based on statistical theory, which is based on the principle of structural risk minimization. Compared with the traditional support vector machine learning method which can avoid the local optimal solution and over fitting characteristics, so there is considerable advantage in solving the small sample, nonlinear and high pattern recognition problems. At the same time, because of these advantages of SVM, more and more researchers began to pay attention to the relevant issues of support vector machines. Through the research in the relevant practice, the researchers found that the parameters of the support vector machine penalty factor and kernel function, the performance of the support vector machine has a great impact. Therefore, it is very important to select the appropriate kernel function and penalty factor for the performance of the support vector machine.0-1 Knapsack problem is a typical combinatorial optimization problem, in the selection of projects, budget control, resource allocation and investment issues such as has very important application, and often as a sub problem of other problems to be studied. There are many methods to solve the 0-1 knapsack problem. The traditional methods such as the implicit enumeration method and branch and bound method, artificial intelligence methods such as neural networks and evolutionary algorithms, etc.. Due to the traditional method in solving large-scale problems with a long iterative time, a large amount of computation, so the artificial intelligence method can achieve better results.Bat algorithm (BA) is a new swarm intelligence optimization algorithm. Compared with other algorithms, BA algorithm has the characteristics of simple structure, good robustness and so on, and it has obvious improvement in the effectiveness and accuracy of the algorithm. However, due to the short time of the bat algorithm, the related research of the bat algorithm is not perfect, and its own existence is easy to fall into local optimum, slow convergence speed and so on. Therefore, some improvement strategies are put forward to improve the optimization efficiency of the bat algorithm in this paper. In view of the shortcomings, such as slow convergence speed in the late evolutionary speed and bat algorithm is easy to fall into local optimum, by introducing the idea of tabu search algorithm proposed a bat algorithm and tabu search algorithm tabu hybrid bat algorithm based on. Tabu search algorithm is to imitate the human memory function, through memory have searched for the local optimal solution of some objects and in further iterative search try to avoid these islands object search, which can in some extent guarantee to explore different effective search path. The related test results show that the hybrid tabu search algorithm can improve the ability of the algorithm to get rid of the local extreme points, the convergence speed and the accuracy of the algorithm.