Abstract
In this paper we propose simple yet efficient version of the two-phase Pareto local search (2PPLS) for solving the biobjective traveling salesman problem (bTSP). In the first phase the powerful Lin–Kernighan heuristic is used to generate some high quality solutions being very close to the Pareto front. Then Pareto local search is used to generate more potentially Pareto efficient solutions along the Pareto front. Instead of previously used method of Aneja and Nair we use uniformly distributed weight vectors in the first phase. We show experimentally that properly balancing the computational effort in the first and second phase we can obtain results better than previous versions of 2PPLS for bTSP and at least comparable to the state-of-the art results of more complex MOMAD method. Furthermore, we propose a simple extension of 2PPLS where some additional solutions are generated by Lin–Kernighan heuristic during the run of PLS. In this way we obtain a method that is more robust with respect to the number of initial solutions generated in the first phase.
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Notes
The source code of the this heuristic is available on http://www.math.uwaterloo.ca/tsp/concorde.
Additional figures for other instances and the R indicator can be found on http://www-desir.lip6.fr/~lustt/Research.html#ProperBalance.
References
Aneja, Y., & Nair, K. (1979). Bicriteria transportation problem. Management Science, 25, 73–78.
Angel, E., Bampis, E., & Gourvès, L. (2004). A dynasearch neighborhood for the bicriteria traveling salesman problem. In X. Gandibleux, M. Sevaux, K. Sörensen, & V. T’kindt (Eds.), Metaheuristics for multiobjective optimisation. Lecture notes in economics and mathematical systems (Vol. 535, pp. 153–176). Berlin: Springer.
Applegate, D. (2003). Chained Lin–Kernighan for large traveling salesman problems. INFORMS Journal on Computing, 15, 82–92.
Battiti, R., Brunato, M., & Mascia, F. (2008). Reactive search and intelligent optimization, operations research/Computer science interfaces. Berlin: Springer. (ISBN 978-0-387-096 23-0).
Coello, C. A. C., & Cortés, N. C. (2005). Solving multiobjective optimization problems using an artificial immune system. Genetic Programming and Evolvable Machines, 6, 163–190.
Czyzak, P., & Jaszkiewicz, A. (1998). Pareto simulated annealing—A metaheuristic technique for multiple-objective combinatorial optimization. Journal of Multi-Criteria Decision Analysis, 7, 34–47.
Deb, K., Agrawal, S., Pratab, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In Proceedings of the parallel problem solving from nature VI conference. Lecture notes in computer science no. 1917 (pp. 849–858). Paris, France: Springer.
Ehrgott, M. (2005). Multicriteria optimization (2nd ed.). Berlin: Springer.
Ehrgott, M., & Gandibleux, X. (2007). Bound sets for biobjective combinatorial optimization problems. Computers & Operations Research, 34, 2674–2694.
Ferguson, T. (1967). Mathematical statistics, a decision theoretic approach. New York: Academic Press.
Hansen, M. P., & Jaszkiewicz, A. (1998). Evaluating the quality of approximations to the non-dominated set. Lyngby: IMM, Department of Mathematical Modelling, Technical University of Denmark.
Jaszkiewicz, A. (2002). On the performance of multiple-objective genetic local search on the 0/1 knapsack problem—A comparative experiment. IEEE Transactions on Evolutionary Computation, 6, 402–412.
Jaszkiewicz, A. (2004). A comparative study of multiple-objective metaheuristics on the bi-objective set covering problem and the Pareto memetic algorithm. Annals of Operations Research, 131, 135–158.
Jaszkiewicz, A., & Zielniewicz, P. (2009). Pareto memetic algorithm with path-relinking for biobjective traveling salesman problem. European Journal of Operational Research, 193, 885–890.
Laporte, G., & Osman, I. (1995). Routing problems: A bibliography. Annals of Operations Research, 61, 227–262.
Lara, A., Sanchez, G., Coello, C. C., & Schutze, O. (2010). HCS: A new local search strategy for memetic multiobjective evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 14, 112–132.
Liangjun, K., Qingfu, Z., & Battiti, R. (2014). Hybridization of decomposition and local search for multiobjective optimization. IEEE Transactions on Cybernetics, 44, 1808–1820.
Lin, S., & Kernighan, B. (1973). An effective heuristic algorithm for the traveling-salesman problem. Operations Research, 21, 498–516.
Lust, T., & Jaszkiewicz, A. (2010). Speed-up techniques for solving large-scale biobjective TSP. Computers & Operations Research, 37, 521–533.
Lust, T., & Teghem, J. (2010). Two-phase Pareto local search for the biobjective traveling salesman problem. Journal of Heuristics, 16, 475–510.
Lust, T., & Teghem, J. (2012). The multiobjective multidimensional knapsack problem: A survey and a new approach. International Transactions in Operational Research, 19, 495–520.
Lust, T., & Tuyttens, D. (2014). Variable and large neighborhood search to solve the multiobjective set covering problem. Journal of Heuristics, 20, 165–188.
Paquete, L., Schiavinotto, T., & Stützle, T. (2007). On local optima in multiobjective combinatorial optimization problems. Annals of Operations Research, 156, 83–97.
Paquete, L., & Stutzle, T. (2006). A study of stochastic local search algorithms for the biobjective QAP with correlated flow matrices. European Journal of Operational Research, 169, 943–959.
Steuer, R. (1986). Multiple criteria optimization: Theory, computation and applications. New York: Wiley.
Ulungu, E., Teghem, J., Fortemps, P., & Tuyttens, D. (1999). MOSA method: A tool for solving multiobjective combinatorial optimization problems. Journal of Multi-Criteria Decision Analysis, 8, 221–236.
Zitzler, E. (1999). Evolutionary algorithms for multiobjective optimization: Methods and applications. Ph.D. thesis, Zurich: Swiss Federal Institute of Technology (ETH).
Zitzler, E., Laumanns, M., Thiele, L., Fonseca, C., & Grunert da Fonseca, V. (2002). Why quality assessment of multiobjective optimizers is difficult. In W. Langdon, E. Cantú-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. Potter, A. Schultz, J. Miller, E. Burke, & N. Jonoska (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO’2002) (pp. 666–673). San Francisco: Morgan Kaufmann Publishers.
Acknowledgements
The research of Andrzej Jaszkiewicz was funded by the the Polish National Science Center, Grant No. UMO-2013/11/B/ST6/01075.
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Jaszkiewicz, A., Lust, T. Proper balance between search towards and along Pareto front: biobjective TSP case study. Ann Oper Res 254, 111–130 (2017). https://doi.org/10.1007/s10479-017-2415-5
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DOI: https://doi.org/10.1007/s10479-017-2415-5