Particle Swarm Optimization Based on Adaptive Diffusion and Hybrid Mutation

Abstract

Conventional algorithms of particle swarm optimization (PSO) are often trapped in local optima in global optimization. In this paper, following an analysis of the main causes of the premature convergence, it proposes a novel PSO algorithm, which is called InformPSO, based on the principles of adaptive diffusion and hybrid mutation. Inspired by the physics of information diffusion, a function is designed to achieve a better particle diversity, by both taking into account their distribution and the number of evolutionary generations and adjusting their social cognitive abilities. Based on genetic self-organization and chaos evolution, clonal selection is built into InformPSO to implement the local evolution of the best particle candidate, gBest, and make use of a Logistic sequence to control the random drift of gBest. These techniques greatly contribute to breaking away from local optima. The global convergence of the algorithm is proved using the theorem of Markov chain. Experiments on optimization of unimodal and multimodal benchmark functions show that, comparing with some other PSO variants, InformPSO converges faster, results in better optima, is more robust, and prevents more effectively the premature