The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
AEFA: Artificial electric field algorithm for global optimization
Abstract
Abstract Electrostatic Force is one of the fundamental force of physical world. The concept of electric field and charged particles provide us a strong theory for the working force of attraction or repulsion between two charged particles. In the recent years many heuristic optimization algorithms are proposed based on natural phenomenon. The current article proposes a novel artificial electric field algorithm (AEFA) which inspired by the Coulomb's law of electrostatic force. The AEFA has been designed to work as a population based optimization algorithm, the concept of charge is extended to fitness value of the population in an innovative way. The proposed AEFA has been tested over a newly and challenging state-of-the-art optimization problems. The theoretical convergence of the proposed AEFA is also established along with statistical validation and comparison with recent state-of-the-art optimization algorithms. The presented study and findings suggests that the proposed AEFA as an outstanding optimization algorithms for non linear optimization.
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Golden jackal optimization: A novel nature-inspired optimizer for engineering applications
Chopra, N., & Mohsin Ansari, M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116924 · Cited by 892
Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm
Amiri, M. H., Mehrabi Hashjin, N., Montazeri, M., Mirjalili, S., & Khodadadi, N. (2024) · Scientific Reports · doi:10.1038/s41598-024-54910-3 · Cited by 718
Rajwar, K., Deep, K., & Das, S. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10470-y · Cited by 687
Political Optimizer: A novel socio-inspired meta-heuristic for global optimization
Askari, Q., Younas, I., & Saeed, M. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105709 · Cited by 615
Thirunavukkarasu, M., Sawle, Y., & Lala, H. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.113192 · Cited by 543
Metadata
- Authors
- Anita, & Yadav, A.
- Venue
- Swarm and Evolutionary Computation
- Publisher
- Elsevier BV
- Published
- 2019-08
- Volume
- 48
- Pages
- 93-108
- ISSN
- 2210-6502 (ISSN-L)
- OpenAlex
- W2922792316
- Cited by
- 411
- References
- 37
- FWCI
- 18.53
- Type
- journal article
- Language
- en
- Open access
- closed
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