全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

Application of Artificial Electric Field Algorithm in Function Optimization

DOI: 10.4236/oalib.1109377, PP. 1-7

Subject Areas: Automata, Mechanical Engineering

Keywords: Artificial Electric Field Algorithm, Optimization, Benchmark Function, Neural Network

Full-Text   Cite this paper   Add to My Lib

Abstract

Artificial electric field (AEF) algorithm is a newly developed heuristic intelligent optimization method, which has the advantages of simple implementation process and less control parameters. So far, it has been applied in some engineering and scientific research fields. For these reasons, AEF algorithm is used to address six benchmark functions to evaluate its search ability. After that, AEF algorithm is combined with BP neural network to find the optimal initial weights and biases, and then the optimized BP network is employed to fit a multi-input single-output nonlinear function. Experimental results indicate that AEF algorithm has good convergence performance and robustness.

Cite this paper

Xu, P. and Cheng, J. (2022). Application of Artificial Electric Field Algorithm in Function Optimization. Open Access Library Journal, 9, e9377. doi: http://dx.doi.org/10.4236/oalib.1109377.

References

[1]  Storn, R. and Price, K. (1997) Differential Evolution—A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11, 341-359. https://doi.org/10.1023/A:1008202821328
[2]  Koyuncu, H. (2020) GM-CPSO: A New Viewpoint to Chaotic Particle Swarm Optimization via Gauss Map. Neural Processing Letters, 52, 241-266. https://doi.org/10.1007/s11063-020-10247-2
[3]  Karaboga, D. and Basturk, B. (2007) A Powerful and Efficient Algorithm for Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm. Journal of Global Optimization, 39, 459-471. https://doi.org/10.1007/s10898-007-9149-x
[4]  Motevali, M.M., Shanghooshabad, A.M., Aram, R.Z. and Keshavarz, H. (2019) WHO: A New Evolutionary Algorithm Bio-Inspired by Wildebeests with a Case Study on Bank Customer Segmentation. International Journal of Pattern Recognition and Artificial Intelligence, 33, Article ID: 1959017. https://doi.org/10.1142/S0218001419590171
[5]  Talatahari, S. and Azizi, M. (2021) Chaos Game Optimization: A Novel Metaheuristic Algorithm. Artificial Intelligence Review, 54, 917-1004. https://doi.org/10.1007/s10462-020-09867-w
[6]  Anita and Yadav, A. (2019) AEFA: Artificial Electric Field Algorithm for Global Optimization. Swarm and Evolutionary Computation, 48, 93-108. https://doi.org/10.1016/j.swevo.2019.03.013
[7]  Houssein, E.H., Hashim, F.A., Ferahtia, S. and Rezk, H. (2021) An Efficient Modified Artificial Electric Field Algorithm for Solving Optimization Problems and Parameter Estimation of Fuel Cell. International Journal of Energy Research, 45, 20199-20218. https://doi.org/10.1002/er.7103
[8]  Anita and Yadav, A. (2020) Discrete Artificial Electric Field Algorithm for High-Order Graph Matching. Applied Soft Computing, 92, Article ID: 106260. https://doi.org/10.1016/j.asoc.2020.106260
[9]  Niroomand, S. (2021) Hybrid Artificial Electric Field Algorithm for Assembly Line Balancing Problem with Equipment Model Selection Possibility. Knowledge-Based Systems, 219, Article ID: 106905. https://doi.org/10.1016/j.knosys.2021.106905
[10]  Das, H., Naik, B. and Behera, H.S. (2021) Optimal Selection of Features Using Artificial Electric Field Algorithm for Classification. Arabian Journal for Science and Engineering, 46, 8355-8369. https://doi.org/10.1007/s13369-021-05486-x
[11]  Li, X.Y. (2022) An Improved Artificial Electricfield Algorithm for Global Optimization. Computer & Digital Engineering, 50, 18-22.
[12]  Cheng, J.T. and Xiong, Y. (2022) Parameter Control Based Cuckoo Search Algorithm for Numerical Optimization. Neural Processing Letters, 54, 3173-3200. https://doi.org/10.1007/s11063-022-10758-0
[13]  Wang, R.L. and Zha, B.B. (2019) A Research on the Optimal Design of BP Neural Network Based on Improved GEP. International Journal of Pattern Recognition and Artificial Intelligence, 33, Article ID: 1959007. https://doi.org/10.1142/S0218001419590079
[14]  Gu, J., Yin, G.H., Huang, P.F., Guo, J.L. and Chen, L.J. (2017) An Improved Back Propagation Neural Network Prediction Model for Subsurface Drip Irrigation System. Computers & Electrical Engineering, 60, 58-65. https://doi.org/10.1016/j.compeleceng.2017.02.016
[15]  Xiong, Y., Cheng, J.T. and Zhang, L.P. (2022) Neighborhood Learning Based Cuckoo Search Algorithm for Global Optimization. International Journal of Pattern Recognition and Artificial Intelligence, 36, Article ID: 2251006. https://doi.org/10.1142/S0218001422510065

Full-Text


comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133

WeChat 1538708413