Abstract
AbstractThe traveling salesman problem (TSP) aims at finding the shortest tour that passes through each vertex in a given graph exactly once. To address TSP, many exact and approximate algorithms have been proposed. In this paper, we propose three new algorithms for TSP based on a genetic algorithm (GA) and an order crossover operator. In the first algorithm, a generic version of a GA with random population is introduced. In the second algorithm, after the random population is introduced, the selected parents are improved with a 2-OPT algorithm and processed further with a GA. Finally, in the third algorithm, the initial solutions are obtained with a nearest neighbor algorithm (NNA) and a nearest insertion algorithm (NIA); afterwards they are improved with a 2-OPT and processed further with a GA. Our approach differs from previous papers for using a GA for TSP in two ways. First, every successive generation of individuals is generated based primarily on 4 best parents from the previous generation regardless the number of individuals in each population. Second, we have proposed the new hybridization between GA, NNA, NIA and 2-OPT. The overall results demonstrate that the proposed GAs offer promising results, particularly for large-sized instances.KeywordsTraveling salesman problemGenetic algorithmNearest neighbor algorithmNearest insertion algorithm2-OPT algorithmHybrid approach
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