원문정보
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초록
영어
Particle Swarm Optimization (PSO) algorithm is a new swarmed intelligent optimization technique, which has been widely used to solve various and complex optimization problems, but there are still premature, low precision, slow convergence phenomenon. We proposed an improved PSO based on update strategy of double extreme value by analyzing the updating ways of double extreme. Improved algorithm has good global searching capability through the classical test function, the new algorithm has solutions of high precision, fast convergence, and it is proved that the new algorithm is feasible and effective.
목차
Abstract
1. Introduction
2. PSO with Contraction Coefficient
3. Double Extremum Update Strategy
3.1 individual extreme update strategy
3.2 Cloning strategy of global extreme variability updating
4. Performance Test Comparing between Improved Particle Swarm Optimization (IPSO) and GA, PSO.
5. Static Function Approximation Problem Experimental Study
6. Conclusions
Acknowledgements
References
1. Introduction
2. PSO with Contraction Coefficient
3. Double Extremum Update Strategy
3.1 individual extreme update strategy
3.2 Cloning strategy of global extreme variability updating
4. Performance Test Comparing between Improved Particle Swarm Optimization (IPSO) and GA, PSO.
5. Static Function Approximation Problem Experimental Study
6. Conclusions
Acknowledgements
References
저자정보
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자료제공 : 네이버학술정보