An Improved Discrete Marine Predator Algorithm for Solving the Traveling Salesman Problem

The Traveling Salesman Problem (TSP) is a classic discrete optimization problem that finds widespread application in logistics, route planning, and other fields. Although traditional optimization methods have performed well in solving TSP, their computational complexity often limits their applicability to large-scale problems. This paper proposes an Improved Discrete Marine Predator Algorithm (IDMPA) to address the TSP. By introducing concepts such as rank-based encoding, equidistant decoding, and sliding alignment, this work extends the continuous optimization capabilities of the original Marine Predator Algorithm(MPA) into the discrete domain. Additionally, the method integrates a 2-Opt algorithm for local search optimization. Experimental results demonstrate that the IDMPA exhibits superior performance across multiple instances in the TSPLIB library, significantly enhancing both solution efficiency and quality. These findings suggest that the IDMPA not only effectively addresses the TSP but also offers a promising approach for tackling other discrete optimization problems.