Discrete sparrow search algorithm for symmetric traveling salesman problem

The traveling salesman problem (TSP) is one of the most intensively studied problems in computational mathematics. This paper proposes a swarm intelligence approach using a discrete sparrow search algorithm (DSSA) with a global perturbation strategy to solve the problem. Firstly, the initial solution in the population is generated by the roulette-wheel selection. Secondly, the order-based decoding method is introduced to complete the update of the sparrow position. Then, the global perturbation mechanism combined with Gaussian mutation and swap operator is adopted to balance exploration and exploitation capability. Finally, the 2-opt local search is integrated to further improve the quality of the solution. Those strategies enhance the solution & rsquo;s quality and accelerate the convergence. Experiments on 34 TSP benchmark datasets are conducted to investigate the performance of the proposed DSSA. And statistical tests are used to verify the significant differences between the proposed DSSA and other state-of-the-art methods. Results show that the proposed method is more competitive and robust in solving the TSP. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a swarm intelligence approach using a discrete sparrow search algorithm (DSSA) with a global perturbation strategy to solve the traveling salesman problem (TSP). Experimental results show that the proposed method is more competitive and robust in solving the TSP.