Research And Application Of Particle Swarm Optimization Algorithm Based On Divided-interval Chaotic Search

Now the scale of optimization problem of real system becomes bigger and bigger, constraint conditions increase continuously and non-linear phenomenon becomes severe, and it's more and more difficult to optimize the system. So the particle swarm optimization algorithm and its improved algorithms have become the hotspots of recent research. Many scholars have done research on it, and have got successful applications in many aspects.In the particle swarm optimization (PSO) model, once the swarm found an optimum, it converges very fast, and is easy to get trapped in local optimum. The system which the PSO model describes doesn't have the mechanism of definitely finding the global optimum, the best particle it finds is just the particle with better fitness value, so PSO algorithm needs improving. One of the methods to improve it is to introduce in the chaos method. The optimization method based on chaotic search uses the ergodicity of the chaos movement, that is, the chaos movement can spread all the status without repeat by its own "rules" in a certain range. It introduces chaos status into optimized variables and then uses chaos variable to search.This thesis analyses the reason why the high precision of solution of chaotic particle swarm optimization(CPSO) algorithm is hard to achieve. Though chaotic particle swarm optimization algorithm lets particles search in the whole variable space, the search scale in final time is too large and the high precision of solution is hard to achieve. This thesis proposes a particle swarm optimization algorithm based on divided-interval chaotic search, it lets the particles search in the selected interval, reduces the scope of the search space, and makes the solution more approximate to the global optimum.The particle swarm optimization algorithm based on divided-interval chaotic search is used to solve Benchmark function optimization problems. Then the method is compared with improved PSO algorithm and CPSO algorithm in the same conditions, which certifies that the particle swarm optimization algorithm based on divided-interval chaotic search has advantage over the above algorithms in the aspects of optimization results and stability, and is a more effective algorithm.On this base, the particle swarm optimization algorithm based on divided-interval chaotic search is used to simulate typical optimization problem in process industry. It solves a typical non-linear program problem in process industry, and has good results in running time and optimization effects. The algorithm has good commonality, and doesn't need special information, so it can be used in process industry successfully.