A modified artificial electric field algorithm and its application DOI
Qiuhong Lin, Lieping Zhang,

Jiatang Cheng

et al.

Physica Scripta, Journal Year: 2024, Volume and Issue: 99(12), P. 125273 - 125273

Published: Nov. 13, 2024

Abstract As an efficient meta-heuristic technique, artificial electric field algorithm (AEFA) has been extensively applied to tackle various challenging tasks posed by practical scenarios. However, in the classical AEFA, fitness function a cumulative effect on charge, resulting limited search capability. To address this issue, modified AEFA (MAEFA) is presented paper. More specifically, novel charge calculation scheme introduced overcome gradually distinguishing charges of particles during evolutionary process. Further, alternating strategy developed calculate total electrostatic force, thereby reinforcing guiding excellent individuals entire population. Subsequently, performance MAEFA investigated using 42 well-benchmarked functions, two chaotic time series prediction problems, and engineering design problems. Experimental results reveal that more competitive comparison with several established AEFAs 20 popular techniques.

Language: Английский

Methods to balance the exploration and exploitation in Differential Evolution from different scales: A survey DOI
Yanyun Zhang, Guanyu Chen, Cheng Li

et al.

Neurocomputing, Journal Year: 2023, Volume and Issue: 561, P. 126899 - 126899

Published: Oct. 7, 2023

Language: Английский

Citations

14
Success History Adaptive Competitive Swarm Optimizer with Linear Population Reduction: Performance benchmarking and application in eye disease detection DOI
Rui Zhong, Zhongmin Wang, Abdelazim G. Hussien

et al.

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 186, P. 109587 - 109587

Published: Jan. 2, 2025

Language: Английский

Citations

0
A modified artificial electric field algorithm and its application DOI
Qiuhong Lin, Lieping Zhang,

Jiatang Cheng

et al.

Physica Scripta, Journal Year: 2024, Volume and Issue: 99(12), P. 125273 - 125273

Published: Nov. 13, 2024

Abstract As an efficient meta-heuristic technique, artificial electric field algorithm (AEFA) has been extensively applied to tackle various challenging tasks posed by practical scenarios. However, in the classical AEFA, fitness function a cumulative effect on charge, resulting limited search capability. To address this issue, modified AEFA (MAEFA) is presented paper. More specifically, novel charge calculation scheme introduced overcome gradually distinguishing charges of particles during evolutionary process. Further, alternating strategy developed calculate total electrostatic force, thereby reinforcing guiding excellent individuals entire population. Subsequently, performance MAEFA investigated using 42 well-benchmarked functions, two chaotic time series prediction problems, and engineering design problems. Experimental results reveal that more competitive comparison with several established AEFAs 20 popular techniques.

Language: Английский

Citations

0