The Research Of Animal Migration Optimization And Its Application

With the development of the society, problems have become more and more complex, and the scale has become greater and greater, the questions are not solved effectively by using the traditional optimization method. The reasonable modeling of optimization problems and effective to solve the problems have become a very important research. Currently, intelligent optimization algorithm for solving complex combinatorial optimization problem has become a new hot spot, it had fast convergence speed and high precision, and it has been adopted to solve complex combination optimization problems by many researchers.Animal migration algorithm is a new type of swarm intelligence algorithm by simulating animal population migration in nature, the algorithm mainly contains migration progress and population update progress. The algorithm has advantages such as simple structure, strong robustness, less parameters, not easy to fall into local optimum, also not fall into the "dimension disaster", etc. At present, animal migration algorithm has been successfully applied to solving complex combinatorial optimization problem. But the algorithm also has some shortcomings, that is, low convergence accuracy and slow convergence speed and so on.In this paper, aiming at the shortcomings of the animal migration algorithm, the algorithm was improved, its purpose is to improve the algorithm's theoretical basis, broaden its range of application. The work of this paper mainly includes the following three aspects:1. Using the dynamic contractions search space in order to enhance the depth of the search performance, by introducing a contraction coefficient parameters can effectively reduce the solution space, and also prompt the population to the optimal individual quickly, thus improved the precision of the algorithm and accelerate the convergence speed of the algorithm.2. Aiming at the low accuracy and easy to fall into local optimum for solving the clustering analysis, this paper adopts dynamic contract way to strengthen the local search ability and avoid the disadvantages of the traditional methods. The experimental results show that the algorithm is feasible and efficient in solving the clustering analysis problems.3. Aiming at the swarm intelligence algorithms easy fall into local optimum, have poor species diversity and dispose the bound term inappropriately in solving constrained optimization functions, in this paper, the animal migration algorithm based on penalty function to solve the constraints. Introducing control parameter can effectively avoid algorithm trapped in local optimum, puts forward the animal migration algorithm for solving constrained optimization problems. Experiment results analysis and comparison show that the proposed algorithm is very effective, has faster convergence speed and optimization accuracy, and could produce good-qualitysolutions even the constraint condition is very harsh.