PSO Optimizer Improvement And Its Application In Image Preprocessing

Particle Swarm Optimization is another new stochastic optimization after genetic algorithm which is also based on evolution. PSO is easy to perform and convergence swiftly and steadily, such good characteristic make it be applied widely in most projects recently and it have performed an important function.Although PSO is used mostly, it has some defects, one of which is that PSO cannot convergence to the global optimum well. This paper adds mutation part to the standard PSO to improve it. And the constrained questions provs that this new PSO is efficient and easier than classical optimal approaches which usually need much strict criteria or have to construct complicated functions.Image enhancing and image restoration are two important parts of image processing. They preprocess the image to make it good for the advanced processing. They are often processed in time domain. In this paper, the new PSO is used to optimize the image which is transformed in frequency domain.