Earticle

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

키워드

저자

• Weidong Ji [ Department of Computer Science and Information Engineering, Harbin Normal University, Harbin, China ]
• Jianhua Wang [ Department of Computer Science and Information Engineering, Harbin Normal University, Harbin, China ]
• Jun Zhang [ Department of Computer Science and Information Engineering, Harbin Normal University, Harbin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605