A Novel Artificial Electric Field Algorithm for Solving Global Optimization and Real-World Engineering Problems.

Abdelazim G Hussien, Adrian Pop, Sumit Kumar, Fatma A Hashim, Gang Hu

Abstract read

In one paragraph

Article in Biomimetics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Abdelazim G HussienDepartment of Computer and Information Science, Linköping University, 581 83 Linköping, Sweden.ORCID 0000-0001-5394-0678

Adrian PopDepartment of Computer and Information Science, Linköping University, 581 83 Linköping, Sweden.ORCID 0000-0003-0091-1181

Sumit KumarAustralian Maritime College, College of Sciences and Engineering, University of Tasmania, Launceston 7248, Australia.ORCID 0000-0003-3042-3779

Fatma A HashimFaculty of Engineering, Helwan University, Cairo 11795, Egypt.

Gang HuDepartment of Applied Mathematics, Xi'an University of Technology, Xi'an 710054, China.ORCID 0000-0003-2274-7482

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Artificial Electric Field Algorithm (AEFA) stands out as a physics-inspired metaheuristic, drawing inspiration from Coulomb's law and electrostatic force; however, while AEFA has demonstrated efficacy, it can face challenges such as convergence issues and suboptimal solutions, especially in high-dimensional problems. To overcome these challenges, this paper introduces a modified version of AEFA, named mAEFA, which leverages the capabilities of Lévy flights, simulated annealing, and the Adaptive

Indexed as

AEFAartificial electric field algorithmescaping local operatorglobal optimization

Identifiers

PMID38534871

DOI10.3390/biomimetics9030186

PMCPMC10968472

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.