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Travelling Salesman Problem (TSP) is the most classically solved problems in computation mathematics in recent years. It is a classical combinatorial optimization and NP-complete problem. TSP is a problem which targets to find a possible tour along which salesman has to visit each city exactly once from a given list of cities and must come back to the starting point of the tour so that total cost spent and distance covered is minimum. In this paper, a hybrid approach combining Genetic Algorithm (GA) with Ant Colony Optimization (ACO) is conferred to solve TSP. In the proposed hybrid GA-ACO algorithm, it is modelled with swap mutation technique and probabilistic selection technique with described update condition. It is enforced to datasets from the standard database of TSPLIB in comparison with the other algorithms in order to appraise the performance of the proposed algorithm. An experiment was carried out and the experimental results show that the method is correct. The empirical results illustrate that the proposed hybrid approach can deal with the TSP well, and the developed mutation process and crossover process are effective.

Design and Development of Hybrid Genetic Algorithm based Ant Colony Optimization Approach for Routing