Multi-objective variable neighborhood search: an application to combinatorial optimization problems

Solutions to real-life optimization problems usually have to be evaluated considering multiple conflicting objectives. These kind of problems, known as multi-objective optimization problems, have been mainly solved in the past by using evolutionary algorithms. In this paper, we explore the adaptatio...

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Bibliographic Details
Published in:Journal of global optimization Vol. 63; no. 3; pp. 515 - 536
Main Authors: Duarte, Abraham, Pantrigo, Juan J., Pardo, Eduardo G., Mladenovic, Nenad
Format: Journal Article
Language:English
Published: New York Springer US 01.11.2015
Springer
Springer Nature B.V
Springer Verlag
Subjects:
Acceptance
Adaptation
Algorithms
Archives & records
Combinatorial analysis
Combinatorics
Computer Science
Criteria
Descent
Genetic algorithms
Mathematical analysis
Mathematics
Mathematics and Statistics
Objectives
Operations Research/Decision Theory
Optimization
Pareto optimum
Proposals
Real Functions
Searching
Studies
Antibandwidth
Multi-objective optimization
Knapsack
Cutwidth
Multi-objective VNS
ISSN:0925-5001, 1573-2916
Online Access:Get full text
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Summary:Solutions to real-life optimization problems usually have to be evaluated considering multiple conflicting objectives. These kind of problems, known as multi-objective optimization problems, have been mainly solved in the past by using evolutionary algorithms. In this paper, we explore the adaptation of the Variable Neighborhood Search (VNS) metaheuristic to solve multi-objective combinatorial optimization problems. In particular, we describe how to design the shake procedure, the improvement method and the acceptance criterion within different VNS schemas (Reduced VNS, Variable Neighborhood Descent and General VNS), when two or more objectives are considered. We validate these proposals over two multi-objective combinatorial optimization problems.
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ISSN:0925-5001
1573-2916
DOI:10.1007/s10898-014-0213-z