Earticle

초록

영어

In standard group search optimizer (GSO) algorithm, scroungers will converge to the similar position if the producer cannot find a better position than the old one in a number of successive iterations and the group may suffer from the premature convergence. In this paper, a hybrid GSO with differential evolution (DE) operator named DEGSO is proposed to enhance the diversity of standard group search optimizer. In this method, the standard GSO algorithm and the DE operator alternate at the odd iterations and at the even iterations. The results of the experiments indicate that DEGSO is competitive to some other evolutionary computation (EA) algorithms.

목차

Abstract
 1. Introduction
 2. Group Search Optimizer
 3. Differential Evolution Algorithm
 4. The Improved GSO with DE
 5. Simulation and Results
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Yu Xie [ School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China ]
• Chunxia Zhao [ School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China ]
• Haofeng Zhang [ School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China ]
• Debao Chen [ School of Physics and Electronic Information, Huaibei Normal University, Huaibei, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605