The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Particle swarm optimization with mutation
Abstract
The particle swarm optimization algorithms converges rapidly during the initial stages of a search, but often slows considerably and can get trapped in local optima. This paper examines the use of mutation to both speed up convergence and escape local minima. It compares the effectiveness of the basic particle swarm optimization scheme (BPSO) with each of BPSO with mutation, constriction particle swarm optimization (CPSO) with mutation, and CPSO without mutation. The four test functions used were the Sphere, Ackley, Rastrigin and Rosenbrock functions of dimensions 10, 20 and 30. The results show that mutation hinders the motion of the swarm on the sphere but the combination of CPSO with mutation provides a significant improvement in performance for the Rastrigin and Rosenbrock functions for all dimensions and the Ackley function for dimensions 20 and 30, with no improvement for the 10 dimensional case.
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Cites
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Parameter selection in particle swarm optimization
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Using selection to improve particle swarm optimization
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Particle swarm optimization with Gaussian mutation
Higashi, N., & Iba, H. (2004) · Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706) · doi:10.1109/sis.2003.1202250 · Cited by 544
Visualizing particle swarm optimization - Gaussian particle swarm optimization
Secrest, B. R., & Lamont, G. B. (2004) · IEEE Swarm Intelligence Symposium · doi:10.1109/sis.2003.1202268 · Cited by 113
The LifeCycle Model: Combining Particle Swarm Optimisation, Genetic Algorithms and HillClimbers
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Cited by
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
Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art
Coello Coello, C. A., & Reyes-Sierra, M. (2006) · International Journal of Computational Intelligence Research · doi:10.5019/j.ijcir.2006.68 · Cited by 1,365
Sengupta, S., Basak, S., & Peters, R. (2018) · Machine Learning and Knowledge Extraction · doi:10.3390/make1010010 · Cited by 543
A review of particle swarm optimization and its applications in Solar Photovoltaic system
Khare, A., & Rangnekar, S. (2013) · Applied Soft Computing · doi:10.1016/j.asoc.2012.11.033 · Cited by 366
Krohling, R. A., & dos Santos Coelho, L. (2006) · IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2006.873185 · Cited by 326
Metadata
- Authors
- Stacey, A., Jancic, M., & Grundy, I.
- Venue
- The 2003 Congress on Evolutionary Computation, 2003. CEC '03.
- Publisher
- IEEE
- Published
- 2004-07-09
- Volume
- 2
- Pages
- 1425-1430
- OpenAlex
- W1545730907
- Cited by
- 252
- References
- 9
- FWCI
- 6.72
- Type
- conference paper
- Language
- en
- Open access
- closed
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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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