Collaborative Variable Neighborhood Search
Details
Serval ID
serval:BIB_9315E4F4A1CC
Type
Inproceedings: an article in a conference proceedings.
Collection
Publications
Institution
Title
Collaborative Variable Neighborhood Search
Title of the conference
Lecture Notes in Computer Science
Publisher
Springer International Publishing
ISBN
9783319916408
9783319916415
9783319916415
ISSN
0302-9743
1611-3349
1611-3349
Publication state
Published
Issued date
2018
Peer-reviewed
Oui
Pages
320-332
Language
english
Abstract
Variable neighborhood search (VNS) is a well-known metaheuristic. Two main ingredients are needed for its design: a collection M=(N1,…,Nr) of neighborhood structures and a local search LS (often 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 variable 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 Neighborhood 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
Create date
12/06/2018 10:08
Last modification date
21/08/2019 6:12