Self-Organization particle swarm optimization based on information feedback

Authors:
Jing Jie;Jianchao Zeng;Chongzhao Han
Affiliations:
School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an City, China;Division of System Simulation & Computer Application, Taiyuan University of Science & Technology, Taiyuan City, China;School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an City, China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Year:
2006

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Abstract

The paper develops a self-organization particle swarm optimization (SOPSO) with the aim to alleviate the premature convergence. SOPSO emphasizes the information interactions between the particle-lever and the swarm-lever, and introduce feedback to simulate the function. Through the feedback information, the particles can perceive the swarm-lever state and adopt favorable behavior model to modify their behavior, which not only can modify the exploitation and the exploration of the algorithm adaptively, but also can vary the diversity of the swarm and contribute to a global optimum output in the swarm. Relative experiments have been done; the results show SOPSO performs very well on benchmark problems, and outperforms the basic PSO in search ability.