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

Vinícius R. Máximo () and Mariá C. V. Nascimento ()
Additional contact information
Vinícius R. Máximo: Universidade Federal de São Paulo (UNIFESP)
Mariá C. V. Nascimento: Universidade Federal de São Paulo (UNIFESP)

Journal of Heuristics, 2019, vol. 25, issue 4, No 3, 539-564

Abstract: 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.

Keywords: Intelligent-guided adaptive search; Path-relinking; Maximum covering location problem; Large scale (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10732-018-9373-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joheur:v:25:y:2019:i:4:d:10.1007_s10732-018-9373-1

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10732

DOI: 10.1007/s10732-018-9373-1

Access Statistics for this article

Journal of Heuristics is currently edited by Manuel Laguna

More articles in Journal of Heuristics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().