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.
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.
基金
supported by the Hi-Tech Research and Development Program of China (2006AA01Z419)
the Major Research Plan of theNational Natural Science Foundation of China (90604023)
the National Laboratory for Modern Communications Science Foundation of China (9140C1101010601)
the Natural Science Foundation of Beijing (4072020)
the National Natural Science Foundation of China (60673098).
