Solving the Traveling Salesman Problem using Reinforced Ant Colony Optimization techniques

This article discusses the results of applying Reinforced Ant Colony Optimization algorithm to solve the Traveling Salesman Problem, an NP Complete problem. To evaluate the performance of Ant Colony Optimization algorithm, comparative studies were done between research which introduced Hybrid Geneti...

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Bibliographic Details
Published inProceedings of the International Conference on Genetic and Evolutionary Methods (GEM) p. 1
Main Authors Poddar, N N, Kaur, D
Format Conference Proceeding
LanguageEnglish
Published Athens The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp) 01.01.2013
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Summary:This article discusses the results of applying Reinforced Ant Colony Optimization algorithm to solve the Traveling Salesman Problem, an NP Complete problem. To evaluate the performance of Ant Colony Optimization algorithm, comparative studies were done between research which introduced Hybrid Genetic algorithm to solve the Traveling Salesman Problem and the original Ant Colony Optimization algorithm proposed by Dorigo. After comparing the Hybrid and Genetic algorithms as well as the Nearest Neighbor algorithm and the original ACO against the reinforced ACO algorithm, it was found that, the reinforced ACO algorithm performs the best with the tour length being the shortest. However, the convergence time for the reinforced ACO algorithm increases when the number of cities increases. Thus, although tour length was shorter for the reinforced ACO, for a large number of cities, the reinforced ACO algorithm took a relatively long time to find a tour.