Hybrid heuristic methods have lately been pointed out as an efficient approach to combinatorial optimization problems. The main reason behind this is that, by combining components from different metaheuristics, it is possible to explore solutions (which would be unreachable without hybridization) in the search space. In particular, evolutionary algorithms may get trapped into local optimum solutions due to the insufficient diversity of the solutions influencing the search process. This paper presents a hybridization of the recently proposed metaheuristic—intelligent-guided adaptive search (IGAS)—with the well-known path-relinking algorithm to solve large scale instances of the maximum covering location problem. Moreover, it proposes a slight change in IGAS that was tested through computational experiments and has shown improvement in its computational cost. Computational experiments also attested that the hybridized IGAS outperforms the results found in the literature.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Aiex, R.M., Resende, M.G.C., Ribeiro, C.C.: TTT plots: a perl program to create time-to-target plots. Optim. Lett. 1, 355–366 (2007)
Church, R., ReVelle, C.: The maximal covering location problem. Pap. Reg. Sci. Assoc. 32, 101–118 (1974)
CPLEX: IBM ILOG CPLEX Optimization Studio CPLEX User’s Manual Version 12 Release 6, IBM (2014)
Dolan, E.D., Moré, J.J.: Benchmarking optimization software with performance profiles. Math. Program. 91, 201–213 (2002)
Feo, T.A., Resende, M.G.C.: A probabilistic heuristic for a computationally difficult set covering problem. Oper. Res. Lett. 8, 67–71 (1989)
Fritzke, B.: Growing cell structures—a self-organizing network for unsupervised and supervised learning. Neural Netw. 7, 1441–1460 (1994)
Fritzke, B.: A growing neural gas network learns topologies. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.): Advances in Neural Information Processing Systems 7, pp. 625–632. MIT Press, Cambridge (1995)
Galvão, R.D., ReVelle, C.: A Lagrangean heuristic for the maximal covering location problem. Eur. J. Oper. Res. 88, 114–123 (1996)
Galvão, R.D., Espejo, L.G.A., Boffey, B.: A comparison of Lagrangean and surrogate relaxations for the maximal covering location problem. Eur. J. Oper. Res. 124, 377–389 (2000)
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., New York (1979)
Glover, F.: Tabu search and adaptive memory programing—advances, applications and challenges. In: Barr, R.S., Helgason, R.V., Kennington, J.L. (eds.) Interfaces in Computer Science and Operations Research, pp. 1–75. Kluwer, Dordrecht (1996)
Glover, F.: A template for scatter search and path relinking. In: Hao, J.-K., Lutton, E., Ronald, E., Schoenauer, M., Snyers, D. (eds.) Artificial Evolution, Volume 1363 of Lecture Notes in Computer Science, pp. 1–51. Springer, Berlin (1998)
Glover, F.: Scatter search and path relinking. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 297–316. McGraw Hill (1999)
Heinke, D., Hamker, F.H.: Comparing neural networks: a benchmark on growing neural gas, growing cell structures, and fuzzy artmap. IEEE Trans. Neural Netw. 9, 1279–1291 (1998)
Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: an automatic algorithm configuration framework. J. Artif. Int. Res. 36, 267–306 (2009)
Jia, H., Ordóñez, F., Dessouky, M.M.: Solution approaches for facility location of medical supplies for large-scale emergencies. Comput. Ind. Eng. 52, 257–276 (2007)
Karasakal, O., Karasakal, E.K.: A maximal covering location model in the presence of partial coverage. Comput. Oper. Res. 31, 1515–1526 (2004)
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59–69 (1982)
Lorena, L.A.N., Pereira, M.A.: A Lagrangean/surrogate heuristic for the maximal covering location problem using Hillman’s edition. Int. J. Ind. Eng. 9, 57–67 (2002)
Martinetz, T.: Competitive Hebbian learning rule forms perfectly topology preserving maps. In: Gielen, S., Kappen, B. (eds.) Proceedings of the International Conference on Artificial Neural Networks (ICANN-93), pp. 427–434. Springer, Amsterdam (1993)
Martinetz, T., Schulten, K.: A “neural-gas” network learns topologies. In: Kohonen, T., Makisara, K., Simula, O., Kangas, J. (eds.) Artificial Neural Networks, pp. 397–402. Elsevier Science Publishers B. V., North-Holland (1991)
Máximo, V.R., Nascimento, M.C.V., Carvalho, A.C.P.L.F.: Intelligent-guided adaptive search for the maximum covering location problem. Comput. Oper. Res. 78, 129–137 (2017)
Pessoa, L.S., Resende, M.G.C., Ribeiro, C.C.: A hybrid Lagrangean heuristic with GRASP and path-relinking for set k-covering. Comput. Oper. Res. 40, 3132–3146 (2013)
Resende, M.G.C.: Computing approximate solutions of the maximum covering problem with GRASP. J. Heurist. 4, 161–177 (1998)
Resende, M.G.C., Ribeiro, C.C.: GRASP with path-relinking: recent advances and applications. In: Ibaraki, T., Nonobe, K., Yagiura, M. (eds.) Metaheuristics: Progress as Real Problem Solvers, Volume 32 of Operations Research/Computer Science Interfaces Series, pp. 29–63. Springer, New York (2005)
Resende, M.G.C., Werneck, R.F.: A hybrid multistart heuristic for the uncapacitated facility location problem. Eur. J. Oper. Res. 174, 54–68 (2006)
ReVelle, C., Scholssberg, M., Williams, J.: Solving the maximal covering location problem with heuristic concentration. Comput. Oper. Res. 35, 427–435 (2008). Part Special Issue: Location Modeling Dedicated to the memory of Charles S. ReVelle
The authors are grateful to Fundação de Amparo á Pesquisa do Estado de São Paulo (FAPESP) (Grant No. 15/21660-4) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (Grant No. 308708/2015-6, 448614/2014-6) for their financial support. Research carried out using the computational resources of the Center for Mathematical Sciences Applied to Industry (CeMEAI) funded by FAPESP (Grant No. 2013/07375-0).
About this article
Cite this article
Máximo, V.R., Nascimento, M.C.V. Intensification, learning and diversification in a hybrid metaheuristic: an efficient unification. J Heuristics 25, 539–564 (2019). https://doi.org/10.1007/s10732-018-9373-1
- Intelligent-guided adaptive search
- Maximum covering location problem
- Large scale