Improved PSO Algorithm And Its Application

Particle Swarm Optimization is a new type algorithm which based on the calculation of the evolution iterated. It is proposed by American Social Psychology Kennedy and Electrical Engineer Eberhart in 1995,the basic idea comes from their early studies of the behavior of birds group, the PSO algorithm has the bigger advantage because its concept is simple, easy to implemented, and has a good convergence rate, more and more scholar are beginning to taking Concern and research on it in recent years, Algorithm is Effective optimization tools on the problem of Nonlinear Continuous Optimization, Combinatorial optimization, Mixed-integer nonlinear optimization, nowdays, it has been widely used in function optimization, the Area of Neural Network Training, Fuzzy System Control, GA, the application In signal processing, pattern recognition, robot activity planning, system design, decision-making, job scheduling, image segmentation, time-frequency analysis of such issues also have been reported.Image enhancement is one of the most basic method in digital image processing , It has two main objectives: one is to improve the image of the visual effects, enhance the clarity of the image components; second is to transformed the image into another form which is more suitable fro persons' viewing and computer automatically analyzes In this paper, Based on the research of the PSO,we proposed two improvements , and make the AIPSO (Adaptive Inverse Panicle Swarm Optimization) algorithm applied to the adaptive image enhancement of gray.Firstly, it demonstrates the main concepts of digital image, the theory and method of image enhancement and the thinking of PSO, then, we give two improvements, On one hand, because the weight w play an important role in iterative equation of pso algorithm, the greater weight is helpful to the speed of convergence, Global search, but difficult to obtain accurate solution, the smaller weight is Helpful to the Local search,it make easy to obtain accurate solution but the convergence is slow, the stratege widely used currenty is that make the weight linearly decreasing by the iterations, we propose a new Evaluation System about Degree of convergence, which we take the different update strategy depending on; on the other hand, deprive from the learning features in people's daily life, we propose a new iterative pattern absenting from error, In specific applications, we construction of two groups at the same time, the first group , we update the particle as what the PSO algorithm doing, the other group, updating the particle take the method of away from the error, and change the information between the two group, complete Optimization task together. The next we use the improved algorithm to test the four classical test functions, compare to the PSO algorithm, it demonstrates that the AIPSO algorithm proposing in this paper has the better convergence rate, optimize performance and has the better accuracy. Finally, for non-complete Beta function which can cover all the typical transform types in image enhancement, we use AIPSO algorithm to achieve the self-adapting choice of its parameters, so as to achieve the self-adapting enhancement of gray image.