A hybrid evolutionary algorithm for traveling salesman problem

This work details the development of a hybrid evolutionary algorithm for solving the traveling salesman problem (TSP). The strategy of the algorithm is to complement and extend the successful results of a genetic algorithm (GA) using a distance preserving crossover (DPX) by incorporating memory in the form of ant pheromone during the city selection process. The synergistic combination of the DPX-GA with city selection based on probability determined by both distance and previous success incorporates additional information into the search mechanism. This combination into a hybrid GA facilitates finding quality solutions for TSP problems with lower computation complexity. This study represents a preliminary investigation with direct comparison to show the feasibility and promise of this hybrid approach.

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