Abstract
Travelling Salesman Problem with profits (TSPP) is a special case of the general Travelling Salesman Problem, all nodes must not be visited, but profit is collected from visited nodes. It is a well-known NP-hard combinatorial optimization problem in the literature. Because of the problem's complexity, exact methods cannot find the global optimum solution to this problem, so many heuristic and metaheuristic solution techniques are studied to find a feasible solution in a reasonable time. In this research, two different metaheuristic algorithms, Simulated Annealing with Many-moves and Variable Neighborhood Search, are proposed to solve the TSPP. Proposed algorithms are tested with three different problem instances and compared in terms of the efficiency of algorithms.
Publisher
Orclever Science and Research Group
Reference16 articles.
1. G. Laporte, "The traveling salesman problem: An overview of exact and approximate algorithms," Eur. J. Oper. Res., vol. 59, no. 2, pp. 231–247, 1992, doi: 10.1016/0377-2217(92)90138-Y.
2. R. Matai, S. Singh, and M. Lal, "Traveling Salesman Problem: an Overview of Applications, Formulations, and Solution Approaches," Travel. Salesm. Probl. Theory Appl., 2010, doi: 10.5772/12909.
3. D. Feillet, P. Dejax, and M. Gendreau, "Traveling salesman problems with profits," Transp. Sci., vol. 39, no. 2, pp. 188–205, 2005, doi: 10.1287/trsc.1030.0079.
4. B. Zhu, J. Suzuki, and P. Boonma, "Solving the Probabilistic Traveling Salesperson Problem with Profits (pTSPP ) with a Noise-aware Evolutionary Multi-objective Optimization Algorithm," 2011.
5. N. Jozefowiez, F. Glover, and M. Laguna, "Multi-objective meta-heuristics for the traveling salesman problem with profits," J. Math. Model. Algorithms, vol. 7, no. 2, pp. 177–195, 2008, doi: 10.1007/s10852-008-9080-2.