Kinetic-molecular theory optimization algorithm using opposition-based learning and varying accelerated motion-Reference-Cited by-同舟云学术

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Kinetic-molecular theory optimization algorithm using opposition-based learning and varying accelerated motion

Published:2020-07-25 Issue:17 Volume:24 Page:12709-12730
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Fan Chaodong,Zheng Ningjun,Zheng Jinhua,Xiao Leyi,Liu Yingnan

Funder

Innovative Research Group Project of the National Natural Science Foundation of China

Hunan Provincial Natural Science Foundation of China

Open Fund Project of Fujian Provincial Key Laboratory of Data Intensive Computing

the Scientific Research Project of Xiangtan University

Foshan Green Intelligent Manufacturing Research Institute of Xiangtan University

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s00500-020-05057-6.pdf

Reference33 articles.

1. Alomoush AA, Alsewari AA, Alamri HS, Zamli KZ, Alomoush W, Younis MI (2020) Modified opposition based learning to improve harmony search variants exploration. In: Advances in intelligent systems and computing, vol 1073. Springer, Cham, pp 279–287

2. Aslimani N, Ellaia R (2018) A new hybrid algorithm combining a new chaos optimization approach with gradient descent for high dimensional optimization problems. Comput Appl Math 37(3):2460–2488

3. Bairathi D, Gopalani D (2020) Random opposition-based learning for computational intelligence. In: Advances in intelligent systems and computing, vol 933. Springer, Singapore, pp 111–120

4. Bianchi L, Dorigo M, Gambardella LM, Gutjahr WJ (2009) A survey on metaheuristics for stochastic combinatory al optimization. Nat Comput Int J 8:239–287

5. Blum C, Puchinger J, Raidl GR, Roli A (2011) Hybrid metaheuristics in combinatorial optimization: a survey. Appl Soft Comput 11:4135–4151

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