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

Mojtaba Ghasemi; Mohsen Zare; Amir Zahedi; Rasul Hemmati; Laith Abualigah; Agostino Forestiero

Pervasive Knowledge and Collective Intelligence on Web and Social Media. First EAI International Conference, PerSOM 2022, Messina, Italy, November 17-18, 2022, Proceedings

Research Article

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

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.1007/978-3-031-31469-8_8,
    author={Mojtaba Ghasemi and Mohsen Zare and Amir Zahedi and Rasul Hemmati and Laith Abualigah and Agostino Forestiero},
    title={A Comparative Study of the Coulomb’s and Franklin’s Laws Inspired Algorithm (CFA) with Modern Evolutionary Algorithms for Numerical Optimization},
    proceedings={Pervasive Knowledge and Collective Intelligence on Web and Social Media. First EAI International Conference, PerSOM 2022, Messina, Italy, November 17-18, 2022, Proceedings},
    proceedings_a={PERSOM},
    year={2023},
    month={4},
    keywords={Evolutionary algorithms (EAs) CFA optimizer Coulomb’s and Franklin’s laws high-dimension group search global numerical optimization},
    doi={10.1007/978-3-031-31469-8_8}
}

Mojtaba Ghasemi
Mohsen Zare
Amir Zahedi
Rasul Hemmati
Laith Abualigah
Agostino Forestiero
Year: 2023
A Comparative Study of the Coulomb’s and Franklin’s Laws Inspired Algorithm (CFA) with Modern Evolutionary Algorithms for Numerical Optimization
PERSOM
Springer
DOI: 10.1007/978-3-031-31469-8_8

Mojtaba Ghasemi¹, Mohsen Zare², Amir Zahedi³, Rasul Hemmati⁴, Laith Abualigah⁵, Agostino Forestiero⁶

1: Department of Electronics and Electrical Engineering
2: Department of Electrical Engineering, Faculty of Engineering, Jahrom University
3: Department of Electrical and Computer Engineering
4: Department of Electrical and Computer Engineering, Marquette University
5: Hourani Center for Applied Scientific Research
6: Institute for High Performance Computing and Networking

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.

Keywords: Evolutionary algorithms (EAs) CFA optimizer Coulomb’s and Franklin’s laws high-dimension group search global numerical optimization

Published: 2023-04-28
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-031-31469-8_8

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

Abstract

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