Abstract
Multi-objective Ant Colony Optimization (MOACO) is a popular algorithm in solving the multi-objective combinational optimization problem. Many variants were introduced to solve different types of multi-objective optimization problem. However, MOACO does not guarantee to generate a good approximation of solution in a predefined termination time. In this paper, Two-Phase Local Search (TPLS) was introduced to cooperate with the MOACO, a two-phase strategy that creates a very good approximation of Pareto Front at the beginning of the algorithm and then further explores the Pareto Front iteratively. We also propose Iterated Local Search – Variable Neighborhood Search (ILS-VNS) as the first phase in TPLS, an iterative improvement process that allows finding improving solutions from adaptively sized neighborhood space. A series of experiments were performed to investigate the performance improvement on the solutions. At the same time, we studied the effect of Weighted Local Search (WLS) and Pareto Local Search (PLS) in the proposed algorithm. The results showed that the newly proposed algorithm obtains a larger area on hypervolume space and exhibits a significantly larger accuracy rate in obtaining the true Pareto-optimal solutions. Additionally, a statistical testing was also performed to verify the significance of the result.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Gambardella, L.M., Taillard, É., Agazzi, G.: MACS-VRPTW: a multiple colony system for vehicle routing problems with time windows. In: New Ideas in Optimization, pp. 63–76. McGraw-Hill (1999)
Dorigo, M., Stützle, T.: Ant Colony Optimization, pp. 33–38. Bradford Company, Scituate (2004)
Paquete, L., Stützle, T.: A two-phase local search for the biobjective traveling salesman problem. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds.) Evolutionary Multi-Criterion Optimization, pp. 479–493. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36970-8_34
Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1, 53–66 (1997)
Ren, L., Duhamel, C., Quilliot, A.: A hybrid ILS/VND Heuristic for the one-commodity pickup-and-delivery traveling salesman problem. Presented at the International Workshop on Green Supply Chain–GSC, Arras, France (2012)
Hoos, H.H., Stützle, T.: Stochastic Local Search: Foundations and Applications. pp. 298–358. Elsevier, Amsterdam (2004)
Dubois-Lacoste, J., López-Ibáñez, M., Stützle, T.: Combining two search paradigms for multi-objective optimization: two-phase and pareto local search. In: Talbi, E.-G. (ed.) Hybrid Metaheuristics, pp. 97–117. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-30671-6_3
Lust, T., Teghem, J., Tuyttens, D.: Very large-scale neighborhood search for solving multiobjective combinatorial optimization problems. In: Takahashi, R.H.C., Deb, K., Wanner, E.F., Greco, S. (eds.) Evolutionary Multi-Criterion Optimization, pp. 254–268. LNCS, vol. 6576, Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-19893-9_18
Lin, S., Kernighan, B.W.: An effective heuristic algorithm for the traveling-salesman problem. Oper. Res. 21, 498–516 (1973)
Jaszkiewicz, A., Lust, T.: Proper balance between search towards and along pareto front: biobjective TSP case study. Ann. Oper. Res. 254(1–2), 111–130 (2017)
López-Ibáñez, M., Paquete, L., Stützle, T.: Hybrid population-based algorithms for the bi-objective quadratic assignment problem. J. Math. Model. Algorithms 5, 111–137 (2006)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm. TIK-report. 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (2001)
Taillard, E.: Robust taboo search for the quadratic assignment problem. Parallel Comput. 17, 443–455 (1991)
Angel, E., Bampis, E., Gourvès, L.: Approximating the pareto curve with local search for the bicriteria TSP(1, 2) problem. Theoret. Comput. Sci. 310, 135–146 (2004)
Bentley, J.J.: Fast algorithms for geometric traveling salesman problems. ORSA J. Comput. 4, 387–411 (1992)
Johnson, D., Mcgeoch, L.: The traveling salesman problem: a case study in local optimization. In: Local Search in Combinatorial Optimization (1997)
Doerner, K., Gutjahr, W.J., Hartl, R.F., Strauss, C., Stummer, C.: Pareto ant colony optimization: a metaheuristic approach to multiobjective portfolio selection. Ann. Oper. Res. 131, 79–99 (2004)
Lopez-Ibanez, M., Stutzle, T.: The automatic design of multiobjective ant colony optimization algorithms. IEEE Trans. Evol. Comput. 16, 861–875 (2012)
Zitzler, E.: Evolutionary algorithms for multiobjective optimization: methods and applications. Ph.D. thesis, Swiss Federal Institute of Technology (ETH), Zurich, Switzerland (1999)
Ariyasingha, I., Fernando, T.: Performance analysis of the multi-objective ant colony optimization algorithms for the traveling salesman problem. Swarm Evol. Comput. 23, 11–26 (2015)
Dubois-Lacoste, J., López-Ibáñez, M., Stützle, T.: Anytime pareto local search. Eur. J. Oper. Res.Eur. J. Oper. Res. 243, 369–385 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Leung, CW., Ng, SC., Lui, A.K. (2018). Combining Two-Phase Local Search with Multi-objective Ant Colony Optimization. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_50
Download citation
DOI: https://doi.org/10.1007/978-3-030-04179-3_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04178-6
Online ISBN: 978-3-030-04179-3
eBook Packages: Computer ScienceComputer Science (R0)