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A Comparative Study of the Coulomb’s and Franklin’s Laws Inspired Algorithm (CFA) with Modern Evolutionary Algorithms for Numerical Optimization

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Pervasive Knowledge and Collective Intelligence on Web and Social Media (PerSOM 2022)

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 at https://www.optim-app.com/projects/cfa, https://www.mathworks.com/matlabcentral/fileexchange/127727-franklin-s-laws-inspired-algorithm-cfa.

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Author information

Authors and Affiliations

  1. Department of Electronics and Electrical Engineering, Shiraz University of Technology, Shiraz, Iran

    Mojtaba Ghasemi

  2. Department of Electrical Engineering, Faculty of Engineering, Jahrom University, Jahrom, Fras, Iran

    Mohsen Zare

  3. Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

    Amir Zahedi

  4. Department of Electrical and Computer Engineering, Marquette University, Milwaukee, Wisconsin, USA

    Rasul Hemmati

  5. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan

    Laith Abualigah

  6. Faculty of Information Technology, Middle East University, Amman, 11831, Jordan

    Laith Abualigah

  7. Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan

    Laith Abualigah

  8. School of Computer Sciences, Universiti Sains Malaysia, 11800, George Town, Pulau Pinang, Malaysia

    Laith Abualigah

  9. Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al Al-Bayt University, Mafraq, 25113, Jordan

    Laith Abualigah

  10. Institute for High Performance Computing and Networking, National Research Council of Italy, Rende, Italy

    Agostino Forestiero

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  1. Mojtaba Ghasemi
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  2. Mohsen Zare
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  4. Rasul Hemmati
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  5. Laith Abualigah
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  6. Agostino Forestiero
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Editor information

Editors and Affiliations

  1. ICAR-CNR, University of Calabria, Rende, Italy

    Carmela Comito

  2. 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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  • DOI: https://doi.org/10.1007/978-3-031-31469-8_8

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-31469-8

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