This paper presents a novel discrete squirrel search optimization algorithm for the bi-objective traveling salesman problem (TSP). Firstly, the squirrel search algorithm, a single-objective optimization algorithm, needs to be improved to a multi-objective optimization algorithm. This paper designs a mapping fitness function according to the Pareto sorting level and grid density to evaluate all the feasible solutions and applies a selection probability based on the roulette wheel selection. Then, this paper implements this algorithm and other algorithms on classic multi-objective test functions to analyze solutions’ convergence and diversity. It is concluded that it has a good performance in solving multi-objective problems. Moreover, based on this multi-objective squirrel search algorithm, this paper then designs an encoding method to initialize solutions, applies a crossover operator to the squirrel migration process, and utilizes a mutation operator to the squirrel mutation stage. In this case, a discrete squirrel search optimization for the bi-objective traveling salesman problem (TSP) is finally designed. And this paper analyzes the results of this algorithm and other algorithms running on classic bi-objective TSPs. As a result, the presented algorithm’s solutions are also superior to other algorithms for convergence and spread.

本文针对双目标旅行商问题 (TSP) 提出了一种新颖的离散松鼠搜索优化算法。首先，松鼠搜索算法是一种单目标优化算法，需要改进为多目标优化算法。本文根据帕累托排序级别和网格密度设计映射适应度函数来评估所有可行解，并应用基于轮盘选择的选择概率。然后，本文在经典的多目标测试函数上实现了该算法和其他算法，以分析解决方案的收敛性和多样性。结论是它在解决多目标问题上有很好的表现。此外，基于这种多目标松鼠搜索算法，然后，本文设计了一种编码方法来初始化解，将交叉算子应用于松鼠迁移过程，并在松鼠变异阶段使用变异算子。在这种情况下，最终设计了针对双目标旅行商问题 (TSP) 的离散松鼠搜索优化。本文分析了该算法和其他算法在经典双目标 TSP 上运行的结果。因此，所提出算法的解决方案在收敛和扩展方面也优于其他算法。本文分析了该算法和其他算法在经典双目标 TSP 上运行的结果。因此，所提出算法的解决方案在收敛和扩展方面也优于其他算法。本文分析了该算法和其他算法在经典双目标 TSP 上运行的结果。因此，所提出算法的解决方案在收敛和扩展方面也优于其他算法。