ARTIFICIAL RAT OPTIMIZATION WITH DECISION-MAKING: A BIO-INSPIRED METAHEURISTIC ALGORITHM FOR SOLVING THE TRAVELING SALESMAN PROBLEM

Abstract

In this paper, we present the Rat Swarm Optimization with Decision Making (HDRSO), a hybrid metaheuristic algorithm inspired by the hunting behavior of rats, for solving the Traveling Salesman Problem (TSP). The TSP is a well-known NP-hard combinatorial optimization problem with important transportation, logistics, and manufacturing systems applications. To improve the search process and avoid getting stuck in local minima, we added a natural mechanism to HDRSO by incorporating crossover and selection operators. In addition, we applied 2-opt and 3-opt heuristics to the best solution found by HDRSO. The performance of HDRSO was evaluated on a set of symmetric instances from the TSPLIB library, and the results demonstrated that HDRSO is a competitive and robust method for solving the TSP, achieving better results than the best-known solutions in some cases.