The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
The Hyper-Cube Framework for Ant Colony Optimization
Abstract
Ant colony optimization is a metaheuristic approach belonging to the class of model-based search algorithms. In this paper, we propose a new framework for implementing ant colony optimization algorithms called the hyper-cube framework for ant colony optimization. In contrast to the usual way of implementing ant colony optimization algorithms, this framework limits the pheromone values to the interval [0,1]. This is obtained by introducing changes in the pheromone value update rule. These changes can in general be applied to any pheromone value update rule used in ant colony optimization. We discuss the benefits coming with this new framework. The benefits are twofold. On the theoretical side, the new framework allows us to prove that in Ant System, the ancestor of all ant colony optimization algorithms, the average quality of the solutions produced increases in expectation over time when applied to unconstrained problems. On the practical side, the new framework automatically handles the scaling of the objective function values. We experimentally show that this leads on average to a more robust behavior of ant colony optimization algorithms.
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Cites
Ant system: optimization by a colony of cooperating agents
Dorigo, M., Maniezzo, V., & Colorni, A. (1996) · IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) · doi:10.1109/3477.484436 · Cited by 12,067
Self-citationBertsekas, D. P. (1997) · Journal of the Operational Research Society · doi:10.1038/sj.jors.2600425 · Cited by 11,726
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
Self-citationEvolutionary Games and Population Dynamics
Hofbauer, J., & Sigmund, K. (1998) · doi:10.1017/cbo9781139173179 · Cited by 5,502
Linear and nonlinear programming.
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Dorigo, M., & Stützle, T. (2004) · The MIT Press eBooks · doi:10.7551/mitpress/1290.001.0001 · Cited by 3,330
Self-citationHoos (2000) · doi:10.1016/s0167-739x(00)00043-1 · Cited by 2,737
Ant colony optimization: a new meta-heuristic
Dorigo, M., & Di Caro, G. (2003) · Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) · doi:10.1109/cec.1999.782657 · Cited by 2,137
Self-citationOR-Library: Distributing Test Problems by Electronic Mail
Beasley, J. E. (1990) · Journal of the Operational Research Society · doi:10.1057/jors.1990.166 · Cited by 1,899
The Traveling Salesman Problem
(2019) · Fundamentals of Supply Chain Theory · doi:10.1002/9781119584445.ch10 · Cited by 1,614
Cited by
Ant Colony Optimization: Overview and Recent Advances
Dorigo, M., & Stützle, T. (2019) · International Series in Operations Research & Management Science · doi:10.1007/978-3-319-91086-4_10 · Cited by 722
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Metadata
- Authors
- Blum, C., & Dorigo, M.
- Venue
- IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics)
- Published
- 2004-04
- Volume
- 34
- Issue
- 2
- Pages
- 1161-1172
- ISSN
- 1083-4419 (ISSN-L)
- OpenAlex
- W2162125251
- Cited by
- 432
- References
- 26
- FWCI
- 25.76
- Type
- journal article
- Language
- en
- Open access
- closed
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