Meta-Heuristic Solution Approaches for Traveling Salesperson Problem - Omar Mohammed Ahmed AHMED; Humar KAHRAMANLI (International Journal of Applied Methods in Electronics and Computers, 2018)
The traveling salesperson problem (TSP) is the NP-hard optimization problems which have been widely studied over the past years. TSP creates a Hamiltonian cycle where each node is visited once and only once to minimize the total traveled distance. TSPs are difficult to be solved using classical mathematical methods. Even with nowadays computers solving TSP problems with these methods takes very plenty of time. Therefore, many efficient optimization methods have been focused for academic proposes for the TSP all the times. Most of the TSP problems are now solved by meta-heuristic methods, that provides a satisfactory solutions in real-time. Meta-heuristic algorithms were inspired from behaviors of animals and insects such as ants, bees, fish schools, bird flocks and mammals.This paper focuses on three meta-heuristic methods: Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO) algorithm and Grey Wolf Optimizer (GWO). The problem for application was selected from TSPLIB. Probably the best implemented solutions were Whale Optimization Algorithm and Grey Wolf Optimizer which can be recommended as primary algorithm to solve the TSP or to start with the meta-heuristic solution