Competitive Elimination Improved Differential Evolution for Wind Farm Layout Optimization Problems
The wind farm layout optimization problem (WFLOP) aims to maximize wind energy utilization efficiency under different wind conditions by optimizing the spatial layout of wind turbines to fully mitigate energy losses caused by wake effects. Some high-performance continuous optimization methods, such...
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MDPI AG
2024-11-01
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| author | Sichen Tao Yifei Yang Ruihan Zhao Hiroyoshi Todo Zheng Tang |
| author_facet | Sichen Tao Yifei Yang Ruihan Zhao Hiroyoshi Todo Zheng Tang |
| author_sort | Sichen Tao |
| collection | DOAJ |
| description | The wind farm layout optimization problem (WFLOP) aims to maximize wind energy utilization efficiency under different wind conditions by optimizing the spatial layout of wind turbines to fully mitigate energy losses caused by wake effects. Some high-performance continuous optimization methods, such as differential evolution (DE) variants, exhibit limited performance when directly applied due to WFLOP’s discrete nature. Therefore, metaheuristic algorithms with inherent discrete characteristics like genetic algorithms (GAs) and particle swarm optimization (PSO) have been extensively developed into current state-of-the-art WFLOP optimizers. In this paper, we propose a novel DE optimizer based on a genetic learning-guided competitive elimination mechanism called CEDE. By designing specialized genetic learning and competitive elimination mechanisms, we effectively address the issue of DE variants failing in the WFLOP due to a lack of discrete optimization characteristics. This method retains the adaptive parameter adjustment capability of advanced DE variants and actively enhances population diversity during convergence through the proposed mechanism, preventing premature convergence caused by non-adaptiveness. Experimental results show that under 10 complex wind field conditions, CEDE significantly outperforms six state-of-the-art WFLOP optimizers, improving the upper limit of power generation efficiency while demonstrating robustness and effectiveness. Additionally, our experiments introduce more realistic wind condition data to enhance WFLOP modeling. |
| format | Article |
| id | doaj-art-d06211b7be474ab88fe76f934c1ceb4d |
| institution | OA Journals |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-d06211b7be474ab88fe76f934c1ceb4d2025-08-20T02:38:48ZengMDPI AGMathematics2227-73902024-11-011223376210.3390/math12233762Competitive Elimination Improved Differential Evolution for Wind Farm Layout Optimization ProblemsSichen Tao0Yifei Yang1Ruihan Zhao2Hiroyoshi Todo3Zheng Tang4Faculty of Engineering, University of Toyama, Toyama 930-8555, JapanFaculty of Science and Technology, Hirosaki University, Hirosaki 036-8560, JapanSchool of Mechanical Engineering, Tongji University, Shanghai 200082, ChinaWicresoft Co., Ltd., Tokyo 163-0445, JapanFaculty of Engineering, University of Toyama, Toyama 930-8555, JapanThe wind farm layout optimization problem (WFLOP) aims to maximize wind energy utilization efficiency under different wind conditions by optimizing the spatial layout of wind turbines to fully mitigate energy losses caused by wake effects. Some high-performance continuous optimization methods, such as differential evolution (DE) variants, exhibit limited performance when directly applied due to WFLOP’s discrete nature. Therefore, metaheuristic algorithms with inherent discrete characteristics like genetic algorithms (GAs) and particle swarm optimization (PSO) have been extensively developed into current state-of-the-art WFLOP optimizers. In this paper, we propose a novel DE optimizer based on a genetic learning-guided competitive elimination mechanism called CEDE. By designing specialized genetic learning and competitive elimination mechanisms, we effectively address the issue of DE variants failing in the WFLOP due to a lack of discrete optimization characteristics. This method retains the adaptive parameter adjustment capability of advanced DE variants and actively enhances population diversity during convergence through the proposed mechanism, preventing premature convergence caused by non-adaptiveness. Experimental results show that under 10 complex wind field conditions, CEDE significantly outperforms six state-of-the-art WFLOP optimizers, improving the upper limit of power generation efficiency while demonstrating robustness and effectiveness. Additionally, our experiments introduce more realistic wind condition data to enhance WFLOP modeling.https://www.mdpi.com/2227-7390/12/23/3762sustainable energywind farm layout optimizationdifferential evolutiongenetic learning competitive elimination strategygenetic algorithmparticle swarm optimization |
| spellingShingle | Sichen Tao Yifei Yang Ruihan Zhao Hiroyoshi Todo Zheng Tang Competitive Elimination Improved Differential Evolution for Wind Farm Layout Optimization Problems Mathematics sustainable energy wind farm layout optimization differential evolution genetic learning competitive elimination strategy genetic algorithm particle swarm optimization |
| title | Competitive Elimination Improved Differential Evolution for Wind Farm Layout Optimization Problems |
| title_full | Competitive Elimination Improved Differential Evolution for Wind Farm Layout Optimization Problems |
| title_fullStr | Competitive Elimination Improved Differential Evolution for Wind Farm Layout Optimization Problems |
| title_full_unstemmed | Competitive Elimination Improved Differential Evolution for Wind Farm Layout Optimization Problems |
| title_short | Competitive Elimination Improved Differential Evolution for Wind Farm Layout Optimization Problems |
| title_sort | competitive elimination improved differential evolution for wind farm layout optimization problems |
| topic | sustainable energy wind farm layout optimization differential evolution genetic learning competitive elimination strategy genetic algorithm particle swarm optimization |
| url | https://www.mdpi.com/2227-7390/12/23/3762 |
| work_keys_str_mv | AT sichentao competitiveeliminationimproveddifferentialevolutionforwindfarmlayoutoptimizationproblems AT yifeiyang competitiveeliminationimproveddifferentialevolutionforwindfarmlayoutoptimizationproblems AT ruihanzhao competitiveeliminationimproveddifferentialevolutionforwindfarmlayoutoptimizationproblems AT hiroyoshitodo competitiveeliminationimproveddifferentialevolutionforwindfarmlayoutoptimizationproblems AT zhengtang competitiveeliminationimproveddifferentialevolutionforwindfarmlayoutoptimizationproblems |