Earticle

초록

영어

Foraging behavior of animal widely concerns researchers. Some swarm intelligence algorithms, such as ant colony optimization algorithm, particle swarm optimization algorithm, artificial fish swarm algorithm, and so on, have been developed. Artificial bee colony algorithm (ABC), which is based on self-organization model, has been proposed. Its application is mainly used in the field of numerical optimization. Researchers verify the outstanding performance in function optimization domain according to the comparison with other algorithms with various improvements. Artificial bee colony algorithm itself has better performance in solving high dimension function. It needs not large population size and can guarantee the global convergence. In the paper, from the view of improving the convergence rate of the algorithm, search operators have been studied and a faster algorithm has been proposed. At the same time, the search region has been optimized. According to the example verification, the new algorithm is effective and the algorithm can be used in the optimization field.

목차

Abstract
 1. Introduction
 2. Standard ABC Algorithm
 3. Modified ABC Algorithm
 4. Adaptive Search Space Introduction
  4.1 Dynamic Adjustments of the Search Space
  4.2 Chaotic Search
 5. Verification
 6. Conclusion
 Acknowledgment
 References

키워드

저자

• Guo Cheng [ Shangluo College, Zhangye, Gansu, 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