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Cited by 239

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Parallel differential evolution

Tasoulis, D.K., Pavlidis, N.G., Plagianakos, V.P., Vrahatis, M.N.·2005·Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)

Abstract

Parallel processing has emerged as a key enabling technology in modern computing. Recent software advances have allowed collections of heterogeneous computers to be used as a concurrent computational resource. In this work we explore how differential evolution can be parallelized, using a ring-network topology, so as to improve both the speed and the performance of the method. Experimental results indicate that the extent of information exchange among subpopulations assigned to different processor nodes, bears a significant impact on the performance of the algorithm. Furthermore, not all the mutation strategies of the differential evolution algorithm are equally sensitive to the value of this parameter.

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Metadata

Authors
Tasoulis, D. K., Pavlidis, N. G., Plagianakos, V. P., & Vrahatis, M. N.
Venue
Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)
Publisher
IEEE
Published
2005-01-17
Pages
2023-2029
OpenAlex
W2154349682
Cited by
239
References
1
FWCI
8.35
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Parallel differential evolution. https://app.cochat.ai/papers/10.1109/cec.2004.1331145
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Tasoulis, D. K., Pavlidis, N. G., Plagianakos, V. P., & Vrahatis, M. N. (2005). Parallel differential evolution. Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753). https://doi.org/10.1109/cec.2004.1331145

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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.