Small World Particle Swarm Optimizer for Global Optimization Problems

Abstract

Particle swarm is a stochastic optimization paradigm inspired by the concepts of social psychology and artificial intelligence. Interrelationship between individuals in a swarm is defined by the population topology, which can be depicted as a network model. Regular networks are highly clustered but the characteristic path length grows linearly with the increase in number of vertices. On the contrary, random networks are not highly clustered but they have small characteristic path length. Small world network have a distinctive combination of regular and random networks i.e., highly clustered and small characteristic path length. This paper takes forward the concept of incorporating small world theory in the Particle Swarm Optimization (PSO) framework. Efficiency of the proposed methodology is tested by applying it on twelve standard benchmark functions. Results obtained are compared with other PSO variants. Comparative study demonstrates the effectiveness of the proposed approach.