Evolutionary, constructive and hybrid procedures for the bi-objective set packing problem

The bi-objective set packing problem is a multi-objective combinatorial optimization problem similar to the well-known set covering/partitioning problems. To our knowledge and surprise, this problem has not yet been studied whereas several applications have been reported. Unfortunately, solving the...

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Bibliographic Details
Published in:European journal of operational research Vol. 204; no. 2; pp. 206 - 217
Main Authors: Delorme, Xavier, Gandibleux, Xavier, Degoutin, Fabien
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 16.07.2010
Elsevier
Elsevier Sequoia S.A
Series:European Journal of Operational Research
Subjects:
Applied sciences
Approximation
Computer Science
Decision theory. Utility theory
Exact sciences and technology
Flows in networks. Combinatorial problems
Genetic algorithms
Heuristic
Hybrid method
Metaheuristics
Multiple objective programming
Multiple objective programming Metaheuristics Set packing problem Hybrid method
Operational research and scientific management
Operational research. Management science
Operations Research
Optimization algorithms
Packing problem
Set packing problem
Studies
Set packing problem
Hybrid method
Metaheuristics
Multiple objective programming
Pareto optimum
Covering problem
Evolutionary algorithm
Multiobjective programming
Algorithmics
Bin packing problem
Coverage
Hybridization
Set partitioning
Randomized algorithm
Combinatorial optimization
Optimal design
Genetic algorithm
Heuristic method
Greedy algorithm
Problem solving
Set covering
Metamodel
ISSN:0377-2217, 1872-6860
Online Access:Get full text
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Summary:The bi-objective set packing problem is a multi-objective combinatorial optimization problem similar to the well-known set covering/partitioning problems. To our knowledge and surprise, this problem has not yet been studied whereas several applications have been reported. Unfortunately, solving the problem exactly in a reasonable time using a generic solver is only possible for small instances. We designed three alternative procedures for approximating solutions to this problem. The first is derived from the original ‘Strength Pareto Evolutionary Algorithm’, which is a population-based metaheuristic. The second is an adaptation of the ‘Greedy Randomized Adaptative Search Procedure’, which is a constructive metaheuristic. As underlined in the overview of the literature summarized here, almost all the recent, effective procedures designed for approximating optimal solutions to multi-objective combinatorial optimization problems are based on a blend of techniques, called hybrid metaheuristics. Thus, the third alternative, which is the primary subject of this paper, is an original hybridization of the previous two metaheuristics. The algorithmic aspects, which differ from the original definition of these metaheuristics, are described, so that our results can be reproduced. The performance of our procedures is reported and the computational results for 120 numerical instances are discussed.
Bibliography:SourceType-Scholarly Journals-1
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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2009.10.014