Human cognition inspired particle swarm optimization algorithm

Abstract

This paper presents a human cognition inspired particle swarm optimization algorithm, and is referred as Cognition Inspired Particle Swarm Optimization (CIPSO). As suggested by the human learning psychology, the particles control the cognition based on their global performance and also the social cognition does not influence one-self directly based on his current knowledge. Hence, in the proposed CIPSO, the particle with global best explores more by only using cognitive component with increasing inertia and self-cognition, where as other particles use explore and exploit using self with entire dimension selection and random social cognition with randomly selected dimensions for updating velocities. The performance of the proposed CIPSO is evaluated using 10 benchmark test functions as suggested in CEC2005 [3]. The performance is also compared with different variants of PSO algorithms reported in the literature. The results clearly indicate that human cognition inspired PSO performs better for most functions than other PSO algorithms reported in the literature.