A new particle swarm optimization algorithm for noisy optimization problems

Author:

Taghiyeh Sajjad,Xu JieORCID

Funder

National Science Foundation

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference78 articles.

1. AlRashidi, M. R., & El-Hawary, M. E. (2009). A survey of particle swarm optimization applications in electric power systems. IEEE Transactions on Evolutionary Computation, 13(4), 913–918.

2. Audibert, J. Y., Munos, R., & Szepesvári, C. (2009). Exploration–exploitation tradeoff using variance estimates in multi-armed bandits. Theoretical Computer Science, 410(19), 1876–1902.

3. Auer, P. (2003). Using confidence bounds for exploitation–exploration trade-offs. The Journal of Machine Learning Research, 3, 397–422.

4. Bartz-Beielstein, T., Blum, D., & Branke, J. (2007). Particle swarm optimization and sequential sampling in noisy environments. In K. F. Doerner, M. Gendreau, P. Greistorfer, W. J. Gutjahr, R. F. Hartl, M. Reimann (Eds.), Metaheuristics, operations research/computer science interfaces series (pp. 261–273). Heidelberg: Springer.

5. Beielstein, T., & Markon, S. (2002). Threshold selection, hypothesis tests, and doe methods. IEEE Proceedings of the World Congress on Computational Intelligence, 1, 777–782.

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