Abstract
Travelling Salesman Problem (TSP) belongs to the class of NP-Complete problems. It has been proved that evolutionary algorithms are effective and efficient, with respect to the traditional methods for solving NP-Complete problems like TSP, with avoidance trapping in local minima areas. Artificial Bee Colony (ABC) is a new swarm-based optimization algorithm, which inspired by the foraging behavior of honey bees. In ABC, the neighborhood search strategy is employed in order to find better solutions around the previous ones. In this paper, a hybrid mutation based mechanism is proposed to perform the neighborhood searching for the bees for the TSP. The introduced approach leads to efficiently reduce the complexity and running time of the ABC algorithm. Experimental results were carried out on the benchmarks from TSPLIB for validation. The findings imply that the proposed approach is able to achieve optimal tours very quickly.
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