An Improved Population Migration Algorithm Introducing Bat Algorithm And The Crossover Operator

With the development of society and progress for people, technology is a glamorous word which is conquering our eyes again and again. Following the continuous development of science and technology, many applied disciplines have emerged and get some achievements. Swarm intelligence optimization algorithm is a representative of many disciplines and has become a hot point in the field of optimization. The majority of swarm intelligence optimization algorithms have completed the theoretical proof. And it has been widely used in dealing with practical optimization. For the algorithm, swarm intelligence optimization algorithm has the characteristics of potential parallelism, and provides a fundamental guarantee for large-scale data processing.Population Migration Algorithm(PMA) is proposed a kind of global optimization simulation principle of population migration for favorable area intelligent algorithm by famous scholar of our country Zhou Yonghua and Mao Zongyuan, in 2003. By a lot of theoretical and experimental verification, compared PMA with the traditional intelligent optimization, there are good convergence capability and strong robustness in solving function optimization and dynamic problem. However, because of PMA lacks of information communication in the process of migration, and randomly scatters point limited in the favorable area and then slowly shrinks it. PMA doesn’t take more effective way to make the search to close to the optimal solution. So PMA is resulted to lack of accuracy and easily falls into the local optimal solution. In order to make the PMA perfect, it is necessary that we study it in theory and practice.For the shortage of PMA in solving problem, this paper made in-depth study and research and proposed some corresponding improvement strategies. And we gave some simulation test functions and got satisfactory results. The main work of the paper can be summarized the following several aspects:First, because the population migration algorithm dealing with the complex functions, is easy to fall into local optimal and has low convergence precision of faults, the author introduces the bat algorithm and the crossover operator to improve the migration of the population algorithm strategy, increasing diversity and effectively avoids the premature problem of population migration algorithm through the organic integration of the improved algorithm has better performance.Secondly, the results show that the improved PMA has more success rates of accurate solution than other algorithm in several complex simulation tests’ data. The whole process of the improved algorithm also shows strongly adaptability, stability robustness and global search ability.