Computational Swarm Intelligence: An Overview and Applications
Benjamin Jeyasurya · 2008
Computational Intelligence is the study of adaptive mechanisms to facilitate intelligent behavior in a complex and changing environments. These mechanisms include artificial neural networks, evolutionary computing, fuzzy systems and swarm intelligence. Much can be learned by studying the behavior of groups, or swarms of biological organisms. The interesting aspect of such swarms is the fact that they exhibit complex collective behavior despite the simplicity of individuals that make up the swarm. The objective of this paper is to focus on one main model inspired from the study of biological systems, namely Particle Swarm Optimization (PSO).PSO is a global optimization approach, modeled on the social behavior of bird flocks. PSO is a population-based search procedure where the individuals, referred to as particles, are grouped into a swarm. Each particle in the swarm represents a candidate solution to the optimization problem. In a PSO system, each particle is “flown’ through the multidimensional search space, adjusting is position according to its own experience and that of neighboring particles. The effect is that particles “fly” toward the global minimum, while still searching a wide area around the best solution. The performance of each particle is measured according to a predefined fitness function which is related to the problem being solved The first part of the paper will present details of the Particle Swarm Optimization algorithm and some case studies in optimization. The paper will then discuss some applications of Particle Swarm Optimization.