A discrete bat algorithm based on Levy flights for Euclidean traveling salesman problem

Bat algorithm is a swarm-intelligence-based metaheuristic proposed in 2010. This algorithm was inspired by echolocation behavior of bats when searching their prey in nature. Since it first introduction, it continues to be used extensively until today, owing to its simplicity, easy handling and applicability to a wide range of problems. However, sometimes the major challenge faced by this technique is can be trapped in a local optimum when facing large complex problems. In this research work, a new discrete bat algorithm is proposed to solve the famous traveling salesman problem as NP-hard combinatorial optimization problem. To enhance the searching strategy and to avoid getting stuck in local minima, random walks based on Le & acute;vy's flights are combined with bat's movement. In addition, to improve the diversity and convergence of the swarm, a neutral crossover operator is embedded to the proposed algorithm. To evaluate the performance of our proposal, two experiments are conducted on 38 benchmark datasets and the obtained results are compared with eight different approaches. Furthermore, the student's t-test, the Friedman's test and the post hoc Wilcoxon's test are performed to check whether there are significant differences between the proposed optimizer and the alternative techniques. The experimental results under comparative studies have shown that, in most cases, the proposed discrete bat algorithm yields significantly better results compared with its competitors.