The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Ant Algorithms for Discrete Optimization
Abstract
This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.
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Citations
Cites
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-citationSimulation and the Monte Carlo Method
Rubinstein, R. Y., & Kroese, D. P. (2016) · Wiley Series in Probability and Statistics · doi:10.1002/9781118631980 · Cited by 3,356 · cited 1×
“Monte Carlo systems [87] are general stochastic simulation systems, that is, techniques performing repeated sampling experiments on the model of the system under consideration by making use of a stochastic component in the state sampling and/or transition rules.”
BackgroundVicsek, T., & Zafeiris, A. (2012) · Physics Reports · doi:10.1016/j.physrep.2012.03.004 · Cited by 2,923
AntNet: Distributed Stigmergetic Control for Communications Networks
Di Caro, G., & Dorigo, M. (1998) · Journal of Artificial Intelligence Research · doi:10.1613/jair.530 · Cited by 1,579
Self-citationGrassé, P.-P. (1959) · Insectes Sociaux · doi:10.1007/bf02223791 · Cited by 1,236
Cited by
Vicsek, T., & Zafeiris, A. (2012) · Physics Reports · doi:10.1016/j.physrep.2012.03.004 · Cited by 2,923
Hoos (2000) · doi:10.1016/s0167-739x(00)00043-1 · Cited by 2,737
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
Self-citationAnt Colony Optimization: Overview and Recent Advances
Dorigo, M., & Stützle, T. (2010) · International Series in Operations Research & Management Science · doi:10.1007/978-1-4419-1665-5_8 · Cited by 579
Self-citationSwarm intelligence based routing protocol for wireless sensor networks: Survey and future directions
Saleem, M., Di Caro, G. A., & Farooq, M. (2011) · Information Sciences · doi:10.1016/j.ins.2010.07.005 · Cited by 386
Metadata
- Authors
- Dorigo, M., Caro, G. D., & Gambardella, L. M.
- Venue
- Artificial Life
- Publisher
- MIT Press - Journals
- Published
- 1999-04
- Volume
- 5
- Issue
- 2
- Pages
- 137-172
- ISSN
- 1064-5462 (ISSN-L)
- 1530-9185
- OpenAlex
- W2118051273
- Cited by
- 2,808
- References
- 5
- FWCI
- 39.06
- Type
- journal article
- Language
- en
- Open access
- closed
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- Dorigo, M., Caro, G. D., & Gambardella, L. M. (1999). Ant Algorithms for Discrete Optimization. Artificial Life. https://doi.org/10.1162/106454699568728
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
