Particle Swarm Optimization with Exhaustive Disturbance Based on Exploration-Exploitation Balance Theory
LI Kun1, LI Ming1,2, CHEN Hao2
1.College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016 2.School of Information Engineering, Nanchang Hangkong University, Nanchang 330063
Abstract Based on the viewpoint that the algorithm gain a good performance only because it fits the characters of the optimization problem, exhaustive disturbance mechanism is introduced in the particle swarm algorithm under the theoretical framework of the exploration-exploitation balance. Based on the thorough researches of the intensity and range for exhaustive disturbance, four kinds of method for employing exhaustive disturbance are proposed in this paper. Some groups of orthogonal experiments are designed to find the best way of employing exhaustive disturbance. By analyzing the experimental results, the following conclusions are drawn. Exhaustive disturbance has its limits while dealing with high dimensional optimization problems, the intensity of exhaustive disturbance needs to be restricted within 15%, and the triggering condition of exhaustive disturbance based on population diversity shows better performance than the other triggering conditions. Finally, on the basis of the above conclusions, adaptive particle swarm optimization with exhaustive disturbance is proposed. Comparing with other algorithms, the proposed algorithm has a better performance.
LI Kun,LI Ming,CHEN Hao. Particle Swarm Optimization with Exhaustive Disturbance Based on Exploration-Exploitation Balance Theory[J]. , 2015, 28(7): 603-612.
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2015, Vol. 28 
