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Metaheuristics in combinatorial optimization

ACM Computing Surveys · 2003 · Vol. 35(3) · pp. 268–308
Christian BlumAndrea Roli

Abstract

The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behavior of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.

Vehicle Routing Optimization MethodsConstraint Satisfaction and OptimizationMetaheuristic Optimization Algorithms ResearchMetaheuristicComputer scienceParallel metaheuristicDiversification (marketing strategy)Optimization problemCombinatorial optimizationField (mathematics)Mathematical optimizationArtificial intelligenceMathematics
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3,106
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References
Combinatorial Optimization: Algorithms and Complexity.
American Mathematical Monthly · 1984 · 6,030 citations
Variable neighborhood search: Principles and applications
European Journal of Operational Research · 2001 · 1,867 citations
An introduction to simulated evolutionary optimization
IEEE Transactions on Neural Networks · 1994 · 1,511 citations
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