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<p>The spherical traveling salesman problem (STSP) is an extension of the classical TSP. This paper proposes a hybrid genetic operators-based mayfly algorithm (HGAMA). In HGAMA, the reverse and crossover operations of GA are introduced, as well as 2-opt and 3-opt operators to enhance the ability of the HGAMA to escape local optima. To evaluate the performance of HGAMA, experiments were conducted in three dimensions covering 9 different cases, and it was compared and analyzed in detail with 12 advanced metaheuristic algorithms. The simulation experiment results demonstrate that HGAMA exhibits higher accuracy and robustness in solving STSP problems. Particularly, in 4 real-world STSP problems, we have successfully identified 34 provincial capitals in China, found the near-optimal routes between 198 national capitals around the world, and also obtained the near-optimal routes for 300 and 400 cities worldwide.</p>

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Genetic Operators-Based Mayfly Algorithm for Real-World Spherical Traveling Salesman Problems

  • 期刊论文

  • 发表日期：

    2025年7月29日

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  • Guo Zhou,
  • Jianjun Zhu,
  • Yongquan Zhou,
  • Qifang Luo

摘要

The spherical traveling salesman problem (STSP) is an extension of the classical TSP. This paper proposes a hybrid genetic operators-based mayfly algorithm (HGAMA). In HGAMA, the reverse and crossover operations of GA are introduced, as well as 2-opt and 3-opt operators to enhance the ability of the HGAMA to escape local optima. To evaluate the performance of HGAMA, experiments were conducted in three dimensions covering 9 different cases, and it was compared and analyzed in detail with 12 advanced metaheuristic algorithms. The simulation experiment results demonstrate that HGAMA exhibits higher accuracy and robustness in solving STSP problems. Particularly, in 4 real-world STSP problems, we have successfully identified 34 provincial capitals in China, found the near-optimal routes between 198 national capitals around the world, and also obtained the near-optimal routes for 300 and 400 cities worldwide.