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Collaborative variable neighborhood search

Gallay, Olivier
Published in Lecture Notes in Computer Science. 2018, vol. 10835, p. 320-332
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 MetaheuristicsVariable Neighborhood Search
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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 https://archive-ouverte.unige.ch/unige:104844

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Deposited on : 2018-06-01

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