A new modified PSO based on black stork foraging process

Abstract

Cognitive parameter plays an important role in particle swarm optimization. Although many cognitive parameter selection strategies are proposed, there is still much work need to do. This paper proposes an individual cognitive parameter setting method by simulating the black stork foraging process. It chooses the cognitive value of each particle associated with its age dominated by its performance. For particles whose performances is better than average performance of the swarm, their cognitive values is set between [1.5, 2.5], while other cognitive values are chosen between [0.5, 1.5]. Simulation results show the modified particle swarm optimization based on this phenomenon is superior to two variants of particle swarm optimization.