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In this study, the performance of five prominent metaheuristic algorithms–Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Sine-Cosine Algorithm (SCA), and Artificial Hummingbird Algorithm (AHA)–is evaluated on the Asymmetric Travelling Salesman Problem (ATSP). These algorithms were chosen for analysis due to their notable achievements in solving various optimization challenges in both theoretical and practical contexts. Originally designed for continuous optimization problem, these methods were adapted to tackle the combinatorial structure of the ATSP using an order-based decoding method. To enhance their local search capabilities, the 2-opt algorithm was also implemented. The performance assessment involved testing each algorithm across 14 distinct ATSP instances and comparing their results. Statistical tests were conducted to verify the performance outcomes, highlighting the AHA as particularly competitive and robust in solving the ATSP.

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Performance Analysis of Metaheuristic Algorithms on Asymmetric Travelling Salesman Problems

  • 会议论文

  • 发表日期：

    2026年

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  • Karuna Panwar,
  • Kusum Deep

摘要

In this study, the performance of five prominent metaheuristic algorithms–Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Sine-Cosine Algorithm (SCA), and Artificial Hummingbird Algorithm (AHA)–is evaluated on the Asymmetric Travelling Salesman Problem (ATSP). These algorithms were chosen for analysis due to their notable achievements in solving various optimization challenges in both theoretical and practical contexts. Originally designed for continuous optimization problem, these methods were adapted to tackle the combinatorial structure of the ATSP using an order-based decoding method. To enhance their local search capabilities, the 2-opt algorithm was also implemented. The performance assessment involved testing each algorithm across 14 distinct ATSP instances and comparing their results. Statistical tests were conducted to verify the performance outcomes, highlighting the AHA as particularly competitive and robust in solving the ATSP.