Intensification, learning and diversification in a hybrid metaheuristic: an efficient unification

Abstract

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.

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Acknowledgements

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).

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Correspondence to Mariá C. V. Nascimento.

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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

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Keywords

  • Intelligent-guided adaptive search
  • Path-relinking
  • Maximum covering location problem
  • Large scale