Acta Metallurgica Sinica(English letters) ›› 2009, Vol. 16 ›› Issue (1): 58-63.doi: 10.1016/S1005-8885(08)60179-X

• Artificial Intelligence • 上一篇    下一篇

Modified chaotic ant swarm to function optimization

李玉英,温巧燕,李丽香   

  1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-02-26
  • 通讯作者: 李玉英

Modified chaotic ant swarm to function optimization

LI Yu-ying, WEN Qiao-yan, LI Li-xiang   

  1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-02-26
  • Contact: LI Yu-ying

摘要:

The chaotic ant swarm algorithm (CAS) is an optimization algorithm based on swarm intelligence theory, and it is inspired by the chaotic and self-organizing behavior of the ants in nature. Based on the analysis of the properties of the CAS, this article proposes a variation on the CAS called the modified chaotic ant swarm (MCAS), which employs two novel strategies to significantly improve the performance of the original algorithm. This is achieved by restricting the variables to search ranges and making the global best ant to learn from different ants’ best information in the end. The simulation of the MCAS on five benchmark functions shows that the MCAS improves the precision of the solution.

关键词:

chaotic;ant;swarm,;benchmark;functions,;modified;chaotic;ant;swarm,;swarm;intelligence

Abstract:

The chaotic ant swarm algorithm (CAS) is an optimization algorithm based on swarm intelligence theory, and it is inspired by the chaotic and self-organizing behavior of the ants in nature. Based on the analysis of the properties of the CAS, this article proposes a variation on the CAS called the modified chaotic ant swarm (MCAS), which employs two novel strategies to significantly improve the performance of the original algorithm. This is achieved by restricting the variables to search ranges and making the global best ant to learn from different ants’ best information in the end. The simulation of the MCAS on five benchmark functions shows that the MCAS improves the precision of the solution.

Key words:

chaotic ant swarm;benchmark functions;modified chaotic ant swarm;swarm intelligence