A Novel Artificial Electric Field Algorithm for Solving Global Optimization and Real-World Engineering Problems
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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
|
| Series: | Biomimetics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-7673/9/3/186 |
| _version_ | 1797241854718115840 |
|---|---|
| author | Abdelazim G. Hussien Adrian Pop Sumit Kumar Fatma A. Hashim Gang Hu |
| author_facet | Abdelazim G. Hussien Adrian Pop Sumit Kumar Fatma A. Hashim Gang Hu |
| author_sort | Abdelazim G. Hussien |
| collection | DOAJ |
| description | 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 <i>s</i>-best Mutation and Natural Survivor Method (NSM) mechanisms. While Lévy flights enhance exploration potential and simulated annealing improves search exploitation, the Adaptive <i>s</i>-best Mutation and Natural Survivor Method (NSM) mechanisms are employed to add more diversity. The integration of these mechanisms in AEFA aims to expand its search space, enhance exploration potential, avoid local optima, and achieve improved performance, robustness, and a more equitable equilibrium between local intensification and global diversification. In this study, a comprehensive assessment of mAEFA is carried out, employing a combination of quantitative and qualitative measures, on a diverse range of 29 intricate CEC’17 constraint benchmarks that exhibit different characteristics. The practical compatibility of the proposed mAEFA is evaluated on five engineering benchmark problems derived from the civil, mechanical, and industrial engineering domains. Results from the mAEFA algorithm are compared with those from seven recently introduced metaheuristic algorithms using widely adopted statistical metrics. The mAEFA algorithm outperforms the LCA algorithm in all 29 CEC’17 test functions with 100% superiority and shows better results than SAO, GOA, CHIO, PSO, GSA, and AEFA in 96.6%, 96.6%, 93.1%, 86.2%, 82.8%, and 58.6% of test cases, respectively. In three out of five engineering design problems, mAEFA outperforms all the compared algorithms, securing second place in the remaining two problems. Results across all optimization problems highlight the effectiveness and robustness of mAEFA compared to baseline metaheuristics. The suggested enhancements in AEFA have proven effective, establishing competitiveness in diverse optimization problems. |
| first_indexed | 2024-04-24T18:29:57Z |
| format | Article |
| id | doaj.art-40a503b573574c0f8fa855dd6e87c127 |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-04-24T18:29:57Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-40a503b573574c0f8fa855dd6e87c1272024-03-27T13:27:44ZengMDPI AGBiomimetics2313-76732024-03-019318610.3390/biomimetics9030186A Novel Artificial Electric Field Algorithm for Solving Global Optimization and Real-World Engineering ProblemsAbdelazim G. Hussien0Adrian Pop1Sumit Kumar2Fatma A. Hashim3Gang Hu4Department of Computer and Information Science, Linköping University, 581 83 Linköping, SwedenDepartment of Computer and Information Science, Linköping University, 581 83 Linköping, SwedenAustralian Maritime College, College of Sciences and Engineering, University of Tasmania, Launceston 7248, AustraliaFaculty of Engineering, Helwan University, Cairo 11795, EgyptDepartment of Applied Mathematics, Xi’an University of Technology, Xi’an 710054, ChinaThe 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 <i>s</i>-best Mutation and Natural Survivor Method (NSM) mechanisms. While Lévy flights enhance exploration potential and simulated annealing improves search exploitation, the Adaptive <i>s</i>-best Mutation and Natural Survivor Method (NSM) mechanisms are employed to add more diversity. The integration of these mechanisms in AEFA aims to expand its search space, enhance exploration potential, avoid local optima, and achieve improved performance, robustness, and a more equitable equilibrium between local intensification and global diversification. In this study, a comprehensive assessment of mAEFA is carried out, employing a combination of quantitative and qualitative measures, on a diverse range of 29 intricate CEC’17 constraint benchmarks that exhibit different characteristics. The practical compatibility of the proposed mAEFA is evaluated on five engineering benchmark problems derived from the civil, mechanical, and industrial engineering domains. Results from the mAEFA algorithm are compared with those from seven recently introduced metaheuristic algorithms using widely adopted statistical metrics. The mAEFA algorithm outperforms the LCA algorithm in all 29 CEC’17 test functions with 100% superiority and shows better results than SAO, GOA, CHIO, PSO, GSA, and AEFA in 96.6%, 96.6%, 93.1%, 86.2%, 82.8%, and 58.6% of test cases, respectively. In three out of five engineering design problems, mAEFA outperforms all the compared algorithms, securing second place in the remaining two problems. Results across all optimization problems highlight the effectiveness and robustness of mAEFA compared to baseline metaheuristics. The suggested enhancements in AEFA have proven effective, establishing competitiveness in diverse optimization problems.https://www.mdpi.com/2313-7673/9/3/186artificial electric field algorithmAEFAescaping local operatorglobal optimization |
| spellingShingle | Abdelazim G. Hussien Adrian Pop Sumit Kumar Fatma A. Hashim Gang Hu A Novel Artificial Electric Field Algorithm for Solving Global Optimization and Real-World Engineering Problems Biomimetics artificial electric field algorithm AEFA escaping local operator global optimization |
| title | A Novel Artificial Electric Field Algorithm for Solving Global Optimization and Real-World Engineering Problems |
| title_full | A Novel Artificial Electric Field Algorithm for Solving Global Optimization and Real-World Engineering Problems |
| title_fullStr | A Novel Artificial Electric Field Algorithm for Solving Global Optimization and Real-World Engineering Problems |
| title_full_unstemmed | A Novel Artificial Electric Field Algorithm for Solving Global Optimization and Real-World Engineering Problems |
| title_short | A Novel Artificial Electric Field Algorithm for Solving Global Optimization and Real-World Engineering Problems |
| title_sort | novel artificial electric field algorithm for solving global optimization and real world engineering problems |
| topic | artificial electric field algorithm AEFA escaping local operator global optimization |
| url | https://www.mdpi.com/2313-7673/9/3/186 |
| work_keys_str_mv | AT abdelazimghussien anovelartificialelectricfieldalgorithmforsolvingglobaloptimizationandrealworldengineeringproblems AT adrianpop anovelartificialelectricfieldalgorithmforsolvingglobaloptimizationandrealworldengineeringproblems AT sumitkumar anovelartificialelectricfieldalgorithmforsolvingglobaloptimizationandrealworldengineeringproblems AT fatmaahashim anovelartificialelectricfieldalgorithmforsolvingglobaloptimizationandrealworldengineeringproblems AT ganghu anovelartificialelectricfieldalgorithmforsolvingglobaloptimizationandrealworldengineeringproblems AT abdelazimghussien novelartificialelectricfieldalgorithmforsolvingglobaloptimizationandrealworldengineeringproblems AT adrianpop novelartificialelectricfieldalgorithmforsolvingglobaloptimizationandrealworldengineeringproblems AT sumitkumar novelartificialelectricfieldalgorithmforsolvingglobaloptimizationandrealworldengineeringproblems AT fatmaahashim novelartificialelectricfieldalgorithmforsolvingglobaloptimizationandrealworldengineeringproblems AT ganghu novelartificialelectricfieldalgorithmforsolvingglobaloptimizationandrealworldengineeringproblems |