원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.7 No.6
2014.12
pp.285-296
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보
