PSO-Based Evolutionary Learning

The new paradigm of Swarm Intelligence, called Particle Swarm Optimization (PSO), is one of the well-known evolutionary computation techniques, which can be considered as an efficient tool to find near optimal solution in a searching space. Especially, PSO is a useful method when the problems to be solved are high-dimensional, nonlinear or some specific information is unavailable. PSO combines the social-only model and the cognition-only model to select the adjustable parameters to approach optimal solution, its main advantage is its rapid convergence and small computational requirements, which make it a good candidate for solving optimization problems. In this book, the efficient, robust, and flexible PSO algorithms are proposed to generate some artificial intelligence system in solving some applications, such as cluster analysis, image processing, and neural network training.

Autorentext

Ching-Yi Chen received his Ph.D. degree from Tamkang University, New Taipei City, Taiwan. He joined the Department of Information and Telecommunications, Ming-Chuan University in 2007 and is now an assistant professor. His main research interests include swarm intelligence, pattern recognition, and embedded systems.