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Modified Ant Colony Optimization Algorithm with Uniform Mutation using Self-Adaptive Approach
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 74 - Number 13 |
| Year of Publication: 2013 |
| Authors: Ramlakhan Singh Jadon, Unmukh Dutta |
10.5120/12943-9931
|
Ramlakhan Singh Jadon, Unmukh Dutta . Modified Ant Colony Optimization Algorithm with Uniform Mutation using Self-Adaptive Approach. International Journal of Computer Applications. 74, 13 ( July 2013), 5-8. DOI=10.5120/12943-9931
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.
References
- A. Colorni, M. Dorigo, V. Maniezzo, "Distributed optimization by ant colonies". Proceedings of European Conference on Artificial Life, Paris, France, pp. 134-142, 1991.
- H. Md. Rais, Z. A. Othman, A. R. Hamdan, Reducing Iteration Using Candidate List, IEEE, 2008.
- H. Md. Rais, Z. A. Othman, A. R. Hamdan, Improvement DACS3 Searching Performance using Local Search,Conference on Data Mining and Optimization, IEEE, 27-28 October 2009.
- J. Han, Y. Tian, An Improved Ant Colony Optimization Algorithm Based on Dynamic Control of Solution Construction and Mergence of Local Search Solutions, Fourth International Conference on Natural Computation, IEEE, 2008
- Storn, R. and K. Price. 1995. Differential Evolution – A Simple and Efficient Adaptive Scheme for Global Optimisation over Continuous Spaces. Technical Report TR-95-012, ICSI. availableviaftp://ftp. icsi. berkeley. edu/pub/techreports/1995/tr-95012. ps. z
- Storn R. and K. Price. 1997. Differential evolution – A Simple and Efficient Heuristic for Global Optimisation over Continuous Spaces. Journal of Global Optimisation, 11(4), 341-359.
- C-MihaelaPintea, D. Dumitrescu, "Improving Ant System Using A Local Updating Rule", Proceedings of theSeventh International Symposium and Numeric Algorithms for Scientific Computing (SYNASC'05), IEEE 2005.
- V. Jakob, T. Ren, "A comparative study of differential evolution, particle swarm optimization, and evolutionaryalgorithms on numerical benchmark problems". Proceedings of Congress on EvolutionaryComputation, vol. 2, Poland, pp. 1980-1987, 2004.
- X-song, B. LI, H. YANG, Improved Ant Colony Algorithm and Its Applications in TSP, Proceedings of Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06), IEEE, 2006.
- Y. Zhang, Z-l. Pei, J-h. Yang, Y-c. Liang, An Improved Ant Colony Optimization Algorithm Based on Route Optimization and Its Applications in Traveling Salesman Problem, IEEE 2007. 1-4244-1509-8.
- R. Gan, Q. Guo, H. Chang, Y. Yi, Improved Ant Colony Optimization Algorithm for the Traveling Salesman Problems, Jouranl of Systems Engineering and Electronics, April 2010, pp 329-333.
- Y. Lin, H. Cai and J. Xiao, "Pseudo Parallel Ant Colony Optimization for Continuous Functions", Third International Conference on Natural Computation (ICNC 2007), 0-7695-2875-9/07.
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