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Proceedings Paper

An adaptive inertia weight strategy for particle swarm optimizer
Author(s): Kaiyou Lei; Fang Wang; Yuhui Qiu; Yi He
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Paper Abstract

The overall performance of Particle Swarm Optimizer lies on its ability to harmonize global and local search process. By dividing the whole swarm into equal sub-swarms with iterative cooperation, and taking a series of Sugeno functions as inertia weight decline curves for each sub-swarm, an adaptive strategy was proposed to adaptively select different inertia decline curve according to the vary rate of the sub-swarm's fitness value. Experimental results on several benchmark functions show that the modified algorithm can effectively balance global and local search ability to avoid premature problem, and obtain better solutions with higher convergence speed.

Paper Details

Date Published: 2 May 2006
PDF: 5 pages
Proc. SPIE 6042, ICMIT 2005: Control Systems and Robotics, 604205 (2 May 2006); doi: 10.1117/12.664515
Show Author Affiliations
Kaiyou Lei, Southwest China Normal Univ. (China)
Fang Wang, Southwest China Normal Univ. (China)
Yuhui Qiu, Southwest China Normal Univ. (China)
Yi He, Southwest China Normal Univ. (China)

Published in SPIE Proceedings Vol. 6042:
ICMIT 2005: Control Systems and Robotics
Yunlong Wei; Kil To Chong; Takayuki Takahashi; Shengping Liu; Zushu Li; Zhongwei Jiang; Jin Young Choi, Editor(s)

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