Perceptive particle swarm optimization: a new learning method from birds seeking

Authors:
Xingjuan Cai;Zhihua Cui;Jianchao Zeng;Ying Tan
Affiliations:
College of Electronic Information Engineering, Taiyuan University of Science and Technology, Shanxi, P.R. China;State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, P.R. China;Division of System Simulation and Computer Application, Taiyuan University of Science and Technology, Shanxi, P.R. China;Division of System Simulation and Computer Application, Taiyuan University of Science and Technology, Shanxi, P.R. China
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

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Abstract

Particle Swarm Optimization (PSO) is a new nature-inspired evolutionary technique simulated with bird flocking and fish schooling. However, the biological model of standard PSO ignores the different decision process of each bird. In nature, if one bird finds some food, generally, it will continue to fly surrounding this spot to find other food, and vice versa. Inspired by this phenomenon, a new swarm intelligent methodology- perceptive particle swarm optimization is designed, in which each particle can apperceive its current status within the whole swarm, and make a dynamic decision by adjusting its next flying direction. Furthermore, a mutation operator is introduced to avoid unsuitable adjustment. Simulation results show the proposed algorithm is effective and efficiency.