🤖 AI Summary
This study addresses the Traveling Salesman Problem (TSP), a classic NP-hard combinatorial optimization challenge, by proposing a high-order relay hybrid solving framework. The approach innovatively integrates the swarm intelligence-based Dragonfly Algorithm (DA) with the memory-driven Tabu Search (TS): DA first performs global exploration to generate high-quality initial solutions, which are subsequently refined through local search via TS. Coupled with a systematic grid-search parameter tuning strategy, the proposed framework demonstrates superior performance on standard TSPLIB instances compared to standalone DA or TS, and outperforms classical metaheuristics such as Genetic Algorithms and Ant Colony Optimization. The results indicate significant improvements in both solution quality and robustness.
📝 Abstract
The Traveling Salesman Problem (TSP) is a classical NP-hard combinatorial optimization problem that aims to find the shortest Hamiltonian cycle visiting each city exactly once and returning to the starting point. This paper proposes a hybrid metaheuristic for the TSP by combining the Dragonfly Algorithm (DA), a swarm-intelligence-based global search method, with Tabu Search (TS), a memory-based local search technique. The proposed method follows a High-Level Relay Hybridization (HRH) scheme, in which DA is first used to explore the solution space and generate a promising initial tour, while TS subsequently refines this solution through neighbourhood-based improvement and tabu memory. The hybrid approach is evaluated on standard TSPLIB benchmark instances, including burma14, att48, and ch150, and compared with standalone DA, standalone TS, and several classical metaheuristics such as Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, and Random Search. A systematic grid-search procedure is also conducted to study the influence of the main hyperparameters on solution quality and execution time. The experimental results indicate that the proposed hybrid can improve tour quality compared with the standalone DA and TS on the tested instances, highlighting the benefit of combining global exploration with local exploitation. However, the results also suggest that performance remains sensitive to parameter settings and problem size, motivating further validation on larger benchmarks and stronger TSP-specific baselines.