Scientific article
English

Collaborative variable neighborhood search

Published inLecture Notes in Computer Science, vol. 10835, p. 320-332
Publication date2018
Abstract

Variable neighborhood search (VNS) is a well-known meta- heuristic. Two main ingredients are needed for its design: a collection M = (N1, . . . ,Nr) of neighborhood structures and a local search LS (of- ten using its own single neighborhood L).M has a diversification purpose (search for unexplored zones of the solution space S), whereas LS plays an intensification role (focus on the most promising parts of S). Usually, the used set M of neighborhood structures relies on the same type of modification (e.g., change the value of i components of the decision vari- able vector, where i is a parameter) and they are built in a nested way (i.e., Ni is included in Ni+1). The more difficult it is to escape from the currently explored zone of S, the larger is i, and the more capability has the search process to visit regions of S which are distant (in terms of solution structure) from the incumbent solution. M is usually designed independently from L. In this paper, we depart from this classical VNS framework and discuss an extension, Collaborative Variable Neighbor- hood Search (CVNS), where the design of M and L is performed in a collaborative fashion (in contrast with nested and independent), and can rely on various and complementary types of modifications (in contrast with a common type with different amplitudes).

Keywords
  • Metaheuristics
  • Variable Neighborhood Search
Citation (ISO format)
ZUFFEREY, Nicolas, GALLAY, Olivier. Collaborative variable neighborhood search. In: Lecture Notes in Computer Science, 2018, vol. 10835, p. 320–332. doi: 10.1007/978-3-319-91641-5_27
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Article (Accepted version)
accessLevelPrivate
Identifiers
Journal ISSN0302-9743
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