The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Handling multiple objectives with particle swarm optimization
Abstract
This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions. Unlike other current proposals to extend PSO to solve multiobjective optimization problems, our algorithm uses a secondary (i.e., external) repository of particles that is later used by other particles to guide their own flight. We also incorporate a special mutation operator that enriches the exploratory capabilities of our algorithm. The proposed approach is validated using several test functions and metrics taken from the standard literature on evolutionary multiobjective optimization. Results indicate that the approach is highly competitive and that can be considered a viable alternative to solve multiobjective optimization problems.
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Citations
Cites
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Cited by
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Zhang, Q., & Li, H. (2007) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2007.892759 · Cited by 10,069
Particle swarm optimization algorithm: An overview
Wang, D., Tan, D., & Liu, L. (2018) · Soft Computing · doi:10.1007/s00500-016-2474-6 · Cited by 3,343
Pareto Multi Objective Optimization
Ngatchou, P., Zarei, A., & El-Sharkawi, A. (2006) · Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems · doi:10.1109/isap.2005.1599245 · Cited by 664
Shukla, A., Verma, K., & Kumar, R. (2019) · IET Generation, Transmission & Distribution · doi:10.1049/iet-gtd.2019.0486 · Cited by 119
MOGOA algorithm for constrained and unconstrained multi-objective optimization problems
Tharwat, A., Houssein, E. H., Ahmed, M. M., Hassanien, A. E., & Gabel, T. (2018) · Applied Intelligence · doi:10.1007/s10489-017-1074-1 · Cited by 114
Metadata
- Authors
- Coello, C. A. C., Pulido, G. T., & Lechuga, M. S.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2004-06
- Volume
- 8
- Issue
- 3
- Pages
- 256-279
- ISSN
- 1089-778X (ISSN-L)
- 1941-0026
- OpenAlex
- W2165171393
- Cited by
- 4,455
- References
- 1
- FWCI
- 54.42
- Type
- journal article
- Language
- en
- Open access
- closed
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- Coello, C. A. C., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/tevc.2004.826067
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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