An Adaptive Particle Swarm Optimization for Global Optimization

The paper suggests a new modified approach to improve the performance of particle swarm optimization (PSO). Inspired by the intelligent behaviors of the natural biotic populations, the modified PSO is based on an adaptive strategy, the particle should stop the inertia movement to enhance the learnin...

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Bibliographic Details
Published inThird International Conference on Natural Computation (ICNC 2007) Vol. 4; pp. 8 - 12
Main Authors Ziyang Zhen, Zhisheng Wang, Yuanyuan Liu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2007
Subjects
Ant colony optimization
Automation
Birds
Constraint optimization
Design optimization
Educational institutions
Equations
Evolutionary computation
Particle swarm optimization
Testing
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Summary:The paper suggests a new modified approach to improve the performance of particle swarm optimization (PSO). Inspired by the intelligent behaviors of the natural biotic populations, the modified PSO is based on an adaptive strategy, the particle should stop the inertia movement to enhance the learning from its experiences and its neighbors when it is found to be in wrong searching direction, and stop the learning process to fly straight when it is found to be the nearest to the destination in the swarm. Furthermore, four different forms of the adaptive PSO model are presented. Comparison results with the standard PSO on the examination of some unconstrained and constrained global optimization functions show the effectiveness of the new modified approach.
ISBN:9780769528755
0769528759
ISSN:2157-9555
DOI:10.1109/ICNC.2007.171