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Erschienen in: Natural Computing 2/2019

25.07.2018

Boost particle swarm optimization with fitness estimation

verfasst von: Lu Li, Yanchun Liang, Tingting Li, Chunguo Wu, Guozhong Zhao, Xiaosong Han

Erschienen in: Natural Computing | Ausgabe 2/2019

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Abstract

It is well known that the classical particle swarm optimization (PSO) is time-consuming when used to solve complex fitness optimization problems. In this study, we perform in-depth research on fitness estimation based on the distance between particles and affinity propagation clustering. In addition, support vector regression is employed as a surrogate model for estimating fitness values instead of using the objective function. The particle swarm optimization algorithm based on affinity propagation clustering, the efficient particle swarm optimization algorithm, and the particle swarm optimization algorithm based on support vector regression machine are then proposed. The experimental results show that the new algorithms significantly reduce the computational counts of the objective function. Compared with the classical PSO, the optimization results exhibit no loss of accuracy or stability.

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Metadaten
Titel
Boost particle swarm optimization with fitness estimation
verfasst von
Lu Li
Yanchun Liang
Tingting Li
Chunguo Wu
Guozhong Zhao
Xiaosong Han
Publikationsdatum
25.07.2018
Verlag
Springer Netherlands
Erschienen in
Natural Computing / Ausgabe 2/2019
Print ISSN: 1567-7818
Elektronische ISSN: 1572-9796
DOI
https://doi.org/10.1007/s11047-018-9699-5

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