Niche search: an application to the Manhattan newspaper problem

João Pedro Pedroso ()
Additional contact information
João Pedro Pedroso: Center for Operations Research and Econometrics (CORE), Université catholique de Louvain (UCL), Louvain la Neuve, Belgium

No 1997065, LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE)

Abstract: In this paper we describe a hybrid strategy for solving combinatorial optimisation problems, obtained by coupling a local search method to an evolutionary algorithm, and we provide an application to the Manhattan newspaper problem. The local search method has been devised specifically for this class of problems. It is based on a composite neighbourhood, which is searched iteratively up to the point where no further improvements can be made. The evolutionary structure is the niche search, an algorithm based on the evolution of several independent niches. Niches whose individuals’ fitness is good remain, and the others tend to be replaced. The separation of the population into niches allows for a good compromise between intensive search (inside each niche) and diversification (through the separation between the niches).

Keywords: Hybrid Evolutionary Algorithms; Vehicle Routing (search for similar items in EconPapers)
Date: 1997-09-01
References: Add references at CitEc
Citations:

Downloads: (external link)
https://sites.uclouvain.be/core/publications/coredp/coredp1997.html (text/html)

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:cor:louvco:1997065

Access Statistics for this paper

More papers in LIDAM Discussion Papers CORE from Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) Voie du Roman Pays 34, 1348 Louvain-la-Neuve (Belgium). Contact information at EDIRC.
Bibliographic data for series maintained by Alain GILLIS ().