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Performance enhancement in solving Traveling Salesman Problem using hybrid genetic algorithm

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2 Author(s)
Kaur, D. ; Dept. of Electr. & Comput. Sci. Eng., Univ. of Toledo, Toledo, OH ; Murugappan, M.M.

In this paper, a novel hybrid genetic algorithm for solving Traveling Salesman Problem (TSP) is presented based on the Nearest Neighbor heuristics and pure Genetic Algorithm (GA). The hybrid genetic algorithm exponentially derives higher quality solutions in relatively shorter time for hard combinatorial real world optimization problems such as Traveling Salesman Problem (TSP) than the pure GA. The hybrid algorithm outperformed the NN algorithm and the pure Genetic Algorithm taken separately. The hybrid genetic algorithm is designed and experimented against the pure GA and the convergence rate improved by more than 200% and the tour distance improved by 17.4% for 90 cities. These results indicate that the hybrid approach is promising and it can be used for various other optimization problems. This algorithm is also independent of the start city of travel whereas the result of NN algorithm are based on start city.

Published in:

Fuzzy Information Processing Society, 2008. NAFIPS 2008. Annual Meeting of the North American

Date of Conference:

19-22 May 2008