Modified Ant Colony Optimization Algorithm with Uniform Mutation using Self-Adaptive Approach
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10.5120/12943-9931 |
Ramlakhan Singh Jadon and Unmukh Dutta. Article: Modified Ant Colony Optimization Algorithm with Uniform Mutation using Self-Adaptive Approach. International Journal of Computer Applications 74(13):5-8, July 2013. Full text available. BibTeX
@article{key:article,
author = {Ramlakhan Singh Jadon and Unmukh Dutta},
title = {Article: Modified Ant Colony Optimization Algorithm with Uniform Mutation using Self-Adaptive Approach},
journal = {International Journal of Computer Applications},
year = {2013},
volume = {74},
number = {13},
pages = {5-8},
month = {July},
note = {Full text available}
}
Abstract
Ant Colony Optimization (ACO) algorithm is a novel meta-heuristic algorithm that has been widely used for different combinational optimization problem and inspired by the foraging behavior of real ant colonies. Ant Colony Optimization has strong robustness and easy to combine with other methods in optimization. In this paper, an efficient ant colony optimization algorithm with uniform mutation operator using self-adaptive approach has been proposed. Here mutation operator is used for enhancing the algorithm escape from local optima. The algorithm converges to the optimal final solution, by gathering the most effective sub-solutions. Experimental results show that the proposed algorithm is better than the algorithm previously proposed.
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