Abstract
This study introduces a hybrid methodology that integrates the ant colony optimization (ACO) with genetic algorithm (GA) techniques. ACO is employed first to create an initial population and to derive a sub-optimal solution for the TSP using a newly designed inver-over (IO) operator. The Proposed IO operator is utilized to improve the solution derived from the ACO. This refined solution is then employed in the GA, where a genetic operator is applied alongside other randomly selected members from the initial population during the second phase. GA is used with the proposed crossover operator and the 2-opt heuristic in this phase to achieve optimal solution refinement towards a global optimum. Our evaluation of the algorithm’s efficacy uses benchmark datasets from TSPLIB. The proposed approach gives superior solution quality, both the average and the best solution metrics, demonstrating enhanced performance with a lower percentage of best error and percentage of average error. Experimental results indicate that the hybrid approach outperforms the efficiency of other state-of-the-art techniques.
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The third author extends their appreciation to the Institute of Excellence, Banaras Hindu University (IoE BHU), for their support and collaborative efforts in carrying out this research.
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Dharm Raj Singh: Implementation, Validation, and Writing-original draft. Manoj Kumar Singh: Conceptualization, Supervision, Writing-review and editing. Sachchida Nand Chaurasia: Writing- draft review and editing, and statistical analysis of results. Pradeepika Verma: Investigation, Visualization, Writing - Review and Editing.
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Singh, D.R., Singh, M.K., Chaurasia, S.N. et al. Hybrid Heuristic for Solving the Euclidean Travelling Salesman Problem. SN COMPUT. SCI. 5, 1050 (2024). https://doi.org/10.1007/s42979-024-03417-9
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DOI: https://doi.org/10.1007/s42979-024-03417-9