Adaptive Particle Swarm Optimization Algorithm And Its Application In Image Segmentation

Particle swarm optimization algorithm is a heuristic global optimization algorithm which appeared recently. It has been widely concerned by people because of its feasibility and effectiveness. It has been proven to be a powerful competitor to other heuristic algorithms, such as genetic algorithm, tabu search and simulated annealing algorithm for global optimization problems. Now it has been applied to many fields of function optimization and engineering technology successfully. However, some limitations in the algorithm have been found by many researchers.Image segmentation is a key basis of many higher level image processing activities such as visualization, compression, and image guided medical diagnoses. Numerous algorithms using different approaches have been proposed for image segmentation. Thresholding is a popular tool for image segmentation for its simplicity, especially in the fields where the real time processing is needed. However, its time-consuming computation is often an obstacle in real time application systems. So it is meaningful to develop an effective algorithm to solve the problem of image segmentation based on thresholding.Based on the former research, the author studies the improvement of particle swarm algorithm and its application in image segmentation. The paper is organized as follows:Part one introduces in detail the status of particle swarm algorithm and image segmentation respectively, followed by the relative basic concept and the main research of this paper.In part two, a novel weighted function has been introduced and some particles are to be updated in a new way when the proposed algorithm traps in local optimum. The proposed algorithm is shown to enhance the convergence speed and global search capability greatly on different benchmark optimization functions. And the proposed algorithm is proven to be effective in solving the problem of image segmentation based on multilevel thresholding.