- Mojtaba Ghasemi17,
- Mohsen Zare18,
- Amir Zahedi19,
- Rasul Hemmati20,
- Laith Abualigah21,22,23,24,25 &
- …
- Agostino Forestiero26
Part of the book series:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ((LNICST,volume 494))
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Abstract
Coulomb and Franklin’s electricity laws are used in this paper to model an efficient optimization algorithm based on electric particle searches, which has been named CFA. For the CFA optimizer, the influence of electrically charged particles on each other in charged things has been predicated on the forces of attraction and repulsion. Evolutionary algorithms (EA) such as hybrid real coded genetic algorithm (RCGA) which combines the global and local search (GL-25), differential evolution (DE) with strategy adaptation (SaDE), composite DE (CoDE), the improved standard particle swarm optimization 2011 (SPSO2013) and the grouped comprehensive learning PSO (GCLPSO) are compared to the CFA optimizer for finding global solutions of seven basic benchmark functions of high dimension D = 50. (GCLPSO). Experiments have shown that the suggested CFA optimizer is quite effective and competitive for the benchmark functions. Note that the source code of the CFA algorithm is publicly available athttps://www.optim-app.com/projects/cfa,https://www.mathworks.com/matlabcentral/fileexchange/127727-franklin-s-laws-inspired-algorithm-cfa.
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Authors and Affiliations
Department of Electronics and Electrical Engineering, Shiraz University of Technology, Shiraz, Iran
Mojtaba Ghasemi
Department of Electrical Engineering, Faculty of Engineering, Jahrom University, Jahrom, Fras, Iran
Mohsen Zare
Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
Amir Zahedi
Department of Electrical and Computer Engineering, Marquette University, Milwaukee, Wisconsin, USA
Rasul Hemmati
Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan
Laith Abualigah
Faculty of Information Technology, Middle East University, Amman, 11831, Jordan
Laith Abualigah
Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan
Laith Abualigah
School of Computer Sciences, Universiti Sains Malaysia, 11800, George Town, Pulau Pinang, Malaysia
Laith Abualigah
Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al Al-Bayt University, Mafraq, 25113, Jordan
Laith Abualigah
Institute for High Performance Computing and Networking, National Research Council of Italy, Rende, Italy
Agostino Forestiero
- Mojtaba Ghasemi
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- Mohsen Zare
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- Amir Zahedi
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- Rasul Hemmati
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- Laith Abualigah
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- Agostino Forestiero
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ICAR-CNR, University of Calabria, Rende, Italy
Carmela Comito
University of Calabria, Rende, Italy
Domenico Talia
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Ghasemi, M., Zare, M., Zahedi, A., Hemmati, R., Abualigah, L., Forestiero, A. (2023). A Comparative Study of the Coulomb’s and Franklin’s Laws Inspired Algorithm (CFA) with Modern Evolutionary Algorithms for Numerical Optimization. In: Comito, C., Talia, D. (eds) Pervasive Knowledge and Collective Intelligence on Web and Social Media. PerSOM 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 494. Springer, Cham. https://doi.org/10.1007/978-3-031-31469-8_8
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