Particle Swarm Optimization with cognitive avoidance component

This paper introduces cognitive avoidance scheme to the Particle Swarm Optimization algorithm. Random movements of particle influenced by personal best solution and global best solution may encourage to take an unfruitful move. This may delay in convergence towards the optimal solution. With the similar notion as particle´s own known best position attracts towards it (cognitive attraction), particle may avoid itself from taking moves around its own known worst position (cognitive avoidance). This concept is added to the standard Particle Swarm Optimization algorithm as cognitive avoidance component with an additional coefficient. Experimental results on well known benchmark functions shows considerable improvement in proposed approach.