The Research Of The Swarm Intelligent Optimization Algorithm And Its Applications

Optimization algorithm is a computer application technology based on mathematics and being used to solve the optimal solution of variety engineering problems. Optimization algorithm has been one of a studying focus in computer domain. The swarm intelligent optimization algorithm, as being one of an important portion of the optimization algorithms, has drawn the scholars'attention in the computer areas. So making a study on the aspect of the swarm intelligent optimization algorithm has a practical significance.The main work and the core of this dissertation are summarized as followings:(1)In the chapter 1, the purpose and the significance of the research on the swarm intelligent optimization algorithm are summarized, and the situation of the swarm intelligent optimization algorithm is discussed. Finally the structure and the contents of this dissertation are given.(2)In the chapter 2, a novel optimization algorithm based on simulating the behavior and the habit of fisher'fishing is presented. This algorithm used three search technologies which are called moving search, reducing search and speeding search. In the beginning, some points are randomly distributed over the grabbling domain, and every point of them is regarded as a"fisher". Then every"fisher"of them is used to search"himself"optimum points or global optimum solution through"his"moving search, reducing search and speeding search independently.(3)In the chapter 3, the path planning scheme based on Artificial Fish-swarm Algorithm is presented. This algorithm is effective and it can achieve the optimal solution over a short period of time. So it can meet the requirement of promptness in the machining path planning.(4)In the chapter 4, a hybrid algorithm combining AFSA and SFOA is presented. The strategy of this algorithm is that the AFSA is used to search the local optimum domain in the beginnings of optimum procedure, and the SFOA is finally made use of determining the global optimization in the local optimum domain found by AFSA. The hybrid algorithm shows an efficient quality in solving global optimization problems.