Earticle

초록

영어

The artificial fish swarm algorithm (AFSA) is a heuristic global optimization technique based on population which is easy to understand, good robustness, and not insensitive to initial values. The behavior of fishes has a great impact on the performance of the algorithm, such as global search and convergence speed. At present, there has no general research theory to select behaviors of fishes. In order to deal with this problem, we proposed an improved artificial fish swarm algorithm based on hybrid behavior selection. There are two mainly works in this paper. Firstly, we propose an improved algorithm based on swallowed behavior, which can greatly speed up the convergence. Secondly, in order to deal with the problems of easy to fall into local optimum value, we added breeding behavior to improve global optimization capability. The experiments on high dimensional function optimization showed that the improved algorithm has more powerful global exploration ability and faster convergence speed.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Introduction TO AFSA
  3.1 Prey behavior
  3.2 Swarm Behavior
  3.3 Follow Behavior
 4. The Improved Algorithm based on Hybrid Behavior Selection (IAFSA)
  4.1 swallowed behavior
  4.2 breeding behavior
 5. Experimental Results
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Zhehuang Huang [ School of Mathematics Sciences, Huaqiao University, Cognitive Science Department, Xiamen University, Xiamen, 361005, China ]
• Yidong Chen [ Cognitive Science Department, Xiamen University, Xiamen, 361005, China, Fujian Key Laboratory of the Brain-like Intelligent Systems, Xiamen, 361005, 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