Artificial electric field algorithm with repulsion mechanism DOI Open Access
G. Y. Zhang,

Jiatang Cheng

Expert Systems, Journal Year: 2024, Volume and Issue: 41(12)

Published: Aug. 19, 2024

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

Language: Английский

Hybrid artificial electric field employing cuckoo search algorithm with refraction learning for engineering optimization problems DOI Creative Commons
Oluwatayomi Rereloluwa Adegboye, Ezgi Deniz Ülker

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: March 12, 2023

Due to its low dependency on the control parameters and straightforward operations, Artificial Electric Field Algorithm (AEFA) has drawn much interest; yet, it still slow convergence solution precision. In this research, a hybrid Employing Cuckoo Search with Refraction Learning (AEFA-CSR) is suggested as better version of AEFA address aforementioned issues. The (CS) method added algorithm boost diversity which may improve global exploration. learning (RL) utilized enhance lead agent can help advance toward optimum local exploitation potential each iteration. Tests are run 20 benchmark functions gauge proposed algorithm's efficiency. order compare other well-studied metaheuristic algorithms, Wilcoxon rank-sum tests Friedman 5% significance level used. evaluate efficiency usability, some significant carried out. As result, overall effectiveness different dimensions populations varied between 61.53 90.0% by overcoming all compared algorithms. Regarding promising results, set engineering problems investigated for further validation our methodology. results proved that AEFA-CSR solid optimizer satisfactory performance.

Language: Английский

Citations

35
A Comprehensive Survey on Artificial Electric Field Algorithm: Theories and Applications DOI
Dikshit Chauhan, Anupam Yadav

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: 31(5), P. 2663 - 2715

Published: Feb. 15, 2024

Language: Английский

Citations

10
Artificial electric field algorithm with repulsion mechanism DOI Open Access
G. Y. Zhang,

Jiatang Cheng

Expert Systems, Journal Year: 2024, Volume and Issue: 41(12)

Published: Aug. 19, 2024

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

Language: Английский

Citations

2