A non-dominated sorting memetic algorithm for the multi-objective travelling salesman problem
This paper considers the multi-objective Traveling Salesman Problem (TSP) where the travel time and monetary cost between cities are considered under minimization. We aim at computing the set of trade-off solutions that fulfil the TSP real-world requirements. To efficiently solve the optimization problem, we introduce a new memetic algorithm based on a combination of two meta-heuristics: simulated annealing based multi-objective algorithm that incorporates the concept of the archive and Non-Dominated Sorting Algorithm NSGAII that incorporates Fast Non-Dominating Sort to handle the convergence of the algorithm toward the optimal Pareto front and crowding distance to maintain a well-distributed set of non-dominated solutions. To assess the performance of the proposed algorithm, a comparative study with an exact method based on the I μ-Constraint method, as well as two existing multi-objective evolutionary algorithms (MOEAs) has been conducted. Several real-world multi-objective TSP instances of varying degrees of difficulty ranging from 29 to 101 cities have been solved to assess the robustness of our algorithm. In particular, the proposed algorithm is found to be significantly superior to existing meta-heuristic algorithms in terms of the quality of the obtained trade-off solutions. Results also show that our algorithm is able to provide high-quality solutions in less amount of computational time compared to other solutions.
ACM International Conference Proceeding Series
Jin, Zian; Dib, Omar; Luo, Yueting; and Hu, Bingxu, "A non-dominated sorting memetic algorithm for the multi-objective travelling salesman problem" (2021). Kean Publications. 846.