Abstract
To enhance the diversity of search space, an improved version of Ant Colony Optimization (ACO), Mean-Contribution Ant System (MCAS) which is derived from Max-Min Ant System (MMAS), is presented in this paper. A new contribution function introduced in MCAS is used to improve the selection strategy of ants and the mechanism “pheromone trails smooth” mentioned by MMAS. Influenced by the improvements, the diversity of search space can be enhanced, which leads to better results. A series of benchmark Traveling Salesman Problems (TSPs) were utilized to test the performances of MCAS and MMAS respectively. The experiment results indicate that MCAS can outperform MMAS in most cases.
This work is partially supported by the National Natural Science Foundation of China, No.70272050.
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Liu, A., Deng, G., Shan, S. (2006). Mean-Contribution Ant System: An Improved Version of Ant Colony Optimization for Traveling Salesman Problem. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_62
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DOI: https://doi.org/10.1007/11903697_62
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