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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 2/2018

13.11.2017 | Original Article

Bat algorithm with triangle-flipping strategy for numerical optimization

verfasst von: Xingjuan Cai, Hui Wang, Zhihua Cui, Jianghui Cai, Yu Xue, Lei Wang

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 2/2018

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Abstract

Bat algorithm (BA) is a novel population-based evolutionary algorithm inspired by echolocation behavior. Due to its simple concept, BA has been widely applied to various engineering applications. As an optimization approach, the global search characteristics determine the optimization performance and convergence speed. In BA, the global search capability is dominated by the velocity updating. How to update the velocity of bats may seriously affect the performance of BA. In this paper, we propose a triangle-flipping strategy to update the velocity of bats. Three different triangle-flipping strategies with five different designs are introduced. The optimization performance is verified by CEC2013 benchmarks in those designs against the standard BA. Simulation results show that the hybrid triangle-flipping strategy is superior to other algorithms.
Vorheriger Artikel Negative transfer detection in transductive transfer learning
Nächster Artikel Active power and reactive power dispatch of wind farm based on wavelet learning

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Metadaten
Titel
Bat algorithm with triangle-flipping strategy for numerical optimization
verfasst von
Xingjuan Cai
Hui Wang
Zhihua Cui
Jianghui Cai
Yu Xue
Lei Wang
Publikationsdatum
13.11.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 2/2018
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-017-0739-8

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