The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
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.
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Cites
An Introduction to Genetic Algorithms
Mitchell, M. (1996) · doi:10.7551/mitpress/3927.001.0001 · Cited by 11,340
Ant colony system: a cooperative learning approach to the traveling salesman problem
Dorigo, M., & Gambardella, L. M. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585892 · Cited by 8,081
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Barr, E. T., Harman, M., McMinn, P., Shahbaz, M., & Yoo, S. (2015) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2014.2372785 · Cited by 1,104
Data Mining for Internet of Things: A Survey
Tsai, C.-W., Lai, C.-F., Chiang, M.-C., & Yang, L. T. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.103013.00206 · Cited by 620
Software Architecture Optimization Methods: A Systematic Literature Review
Aleti, A., Buhnova, B., Grunske, L., Koziolek, A., & Meedeniya, I. (2013) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2012.64 · Cited by 293
Metaheuristic Algorithms in Modeling and Optimization
Gandomi, A. H., Yang, X.-S., Talatahari, S., & Alavi, A. H. (2013) · Metaheuristic Applications in Structures and Infrastructures · doi:10.1016/b978-0-12-398364-0.00001-2 · Cited by 268
Shaikh, P. W., El-Abd, M., Khanafer, M., & Gao, K. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3014296 · Cited by 183
Metadata
- Authors
- Blum, C., & Roli, A.
- Venue
- ACM Computing Surveys
- Published
- 2003-09
- Volume
- 35
- Issue
- 3
- Pages
- 268-308
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2004617458
- Cited by
- 3,236
- References
- 3
- FWCI
- 44.88
- Type
- journal article
- Language
- en
- Open access
- closed
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