New heuristic techniques for solving the multi-objective travelling salesman problem

The Multi-Objective Travelling Salesman Problem (MOTSP) is recognized as an NP-hard combinatorial optimization problem. In this study, we introduce a set of novel heuristic algorithms designed to address the MOTSP efficiently. The performance of these heuristics is rigorously assessed and compared with exact optimization techniques, particularly the Branch-and-Cut method, which remains one of the most powerful exact approaches currently available. Experimental evaluations on optimal benchmark datasets demonstrate the effectiveness and computational superiority of the proposed heuristics, notably in reducing CPU time while maintaining high-quality solutions.