A Comprehensive Survey on Artificial Electric Field Algorithm: Theories and Applications

Chauhan, Dikshit; Yadav, Anupam

doi:10.1007/s11831-023-10058-3

Arch Computat Methods Eng

2024

DOI: 10.1007/s11831-023-10058-3

|View full text |Cite

A Comprehensive Survey on Artificial Electric Field Algorithm: Theories and Applications

Dikshit Chauhan,

Anupam Yadav

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 151 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Artificial electric field algorithm with repulsion mechanism

Zhang,

Cheng

2024

Expert Systems

View full text Add to dashboard Cite

Due to its outstanding performance in addressing optimization problems, artificial electric field (AEF) algorithm has garnered increasing notice in recent years. Nevertheless, numerous studies indicate that AEF is susceptible to premature convergence when the region influenced by the global optimum constitutes a small fraction of the entire solution space. By conducting micro‐level research on the particles during the evolution process of AEF, it is revealed that the primary factors influencing optimization performance are the Coulomb's electrostatic force mechanism and the fixed attenuation factor. Inspired by this observation, we propose an improved version named artificial electric field algorithm with repulsion mechanism (RMAEF). Specifically, in RMAEF, a repulsion mechanism is incorporated to make particles escape from local optima. Furthermore, an adaptive attenuation factor is employed to update dynamically Coulomb's constant. RMAEF is compared with AEF and its state‐of‐art variants under 44 test functions from CEC 2005 and CEC 2014 test suites. From the experiment results, it is obvious that among 14 benchmark functions from CEC 2005 on 30D and 50D optimization, the RMAEF algorithm exhibits superior performance on 8 and 9 functions compared with advanced variants of AEF. For CEC 2014 on 30D and 50D optimization, the RMAEF algorithm produces the best results on 11 and 12 functions, respectively. In addition, three real‐world problems are also used to verify the versatility and robustness. The results demonstrate that RMAEF outperforms its competitors in terms of overall performance.

show abstract

Artificial electric field algorithm with repulsion mechanism

Zhang,

Cheng

2024

Expert Systems

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A Comprehensive Survey on Artificial Electric Field Algorithm: Theories and Applications

Cited by 1 publication

References 151 publications

Artificial electric field algorithm with repulsion mechanism

Artificial electric field algorithm with repulsion mechanism

Contact Info

Product

Resources

About