A State-of-the-Art Fractional Order-Driven Differential Evolution for Wind Farm Layout Optimization
The wind farm layout optimization problem (WFLOP) aims to maximize wind energy utilization efficiency and mitigate energy losses caused by wake effects by optimizing the spatial layout of wind turbines. Although Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been widely used in...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/2/282 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588061017374720 |
|---|---|
| author | Sichen Tao Sicheng Liu Ruihan Zhao Yifei Yang Hiroyoshi Todo Haichuan Yang |
| author_facet | Sichen Tao Sicheng Liu Ruihan Zhao Yifei Yang Hiroyoshi Todo Haichuan Yang |
| author_sort | Sichen Tao |
| collection | DOAJ |
| description | The wind farm layout optimization problem (WFLOP) aims to maximize wind energy utilization efficiency and mitigate energy losses caused by wake effects by optimizing the spatial layout of wind turbines. Although Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been widely used in WFLOP due to their discrete optimization characteristics, they still have limitations in global exploration capability and optimization depth. Meanwhile, the Differential Evolution algorithm (DE), known for its strong global optimization ability and excellent performance in handling complex nonlinear problems, is well recognized in continuous optimization issues. However, since DE was originally designed for continuous optimization scenarios, it shows insufficient adaptability under the discrete nature of WFLOP, limiting its potential advantages. In this paper, we propose a Fractional-Order Difference-driven DE Optimization Algorithm called FODE. By introducing the memory and non-local properties of fractional-order differences, FODE effectively overcomes the adaptability issues of advanced DE variants in WFLOP’s discreteness while organically applying their global optimization capabilities for complex nonlinear problems to WFLOP to achieve more efficient overall optimization performance. Experimental results show that under 10 complex wind farm conditions, FODE significantly outperforms various current state-of-the-art WFLOP algorithms including GA, PSO, and DE variants in terms of optimization performance, robustness, and applicability. Incorporating more realistic wind speed distribution and wind condition data into modeling and experiments, further enhancing the realism of WFLOP studies presented here, provides a new technical pathway for optimizing wind farm layouts. |
| format | Article |
| id | doaj-art-649bb2a26c8d405588e8923d310f51f6 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-649bb2a26c8d405588e8923d310f51f62025-01-24T13:40:01ZengMDPI AGMathematics2227-73902025-01-0113228210.3390/math13020282A State-of-the-Art Fractional Order-Driven Differential Evolution for Wind Farm Layout OptimizationSichen Tao0Sicheng Liu1Ruihan Zhao2Yifei Yang3Hiroyoshi Todo4Haichuan Yang5Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, JapanFaculty of Engineering, University of Toyama, Toyama-shi 930-8555, JapanSchool of Mechanical Engineering, Tongji University, Shanghai 200082, ChinaFaculty of Science and Technology, Hirosaki University, Hirosaki 036-8560, JapanWicresoft Co., Ltd., Tokyo 163-0445, JapanGraduate School of Technology, Industrial and Social Sciences, Tokushima University, Tokushima 770-8506, JapanThe wind farm layout optimization problem (WFLOP) aims to maximize wind energy utilization efficiency and mitigate energy losses caused by wake effects by optimizing the spatial layout of wind turbines. Although Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been widely used in WFLOP due to their discrete optimization characteristics, they still have limitations in global exploration capability and optimization depth. Meanwhile, the Differential Evolution algorithm (DE), known for its strong global optimization ability and excellent performance in handling complex nonlinear problems, is well recognized in continuous optimization issues. However, since DE was originally designed for continuous optimization scenarios, it shows insufficient adaptability under the discrete nature of WFLOP, limiting its potential advantages. In this paper, we propose a Fractional-Order Difference-driven DE Optimization Algorithm called FODE. By introducing the memory and non-local properties of fractional-order differences, FODE effectively overcomes the adaptability issues of advanced DE variants in WFLOP’s discreteness while organically applying their global optimization capabilities for complex nonlinear problems to WFLOP to achieve more efficient overall optimization performance. Experimental results show that under 10 complex wind farm conditions, FODE significantly outperforms various current state-of-the-art WFLOP algorithms including GA, PSO, and DE variants in terms of optimization performance, robustness, and applicability. Incorporating more realistic wind speed distribution and wind condition data into modeling and experiments, further enhancing the realism of WFLOP studies presented here, provides a new technical pathway for optimizing wind farm layouts.https://www.mdpi.com/2227-7390/13/2/282sustainable energywind farm layout optimizationdifferential evolutiongenetic learning competitive elimination strategygenetic algorithmparticle swarm optimization |
| spellingShingle | Sichen Tao Sicheng Liu Ruihan Zhao Yifei Yang Hiroyoshi Todo Haichuan Yang A State-of-the-Art Fractional Order-Driven Differential Evolution for Wind Farm Layout Optimization Mathematics sustainable energy wind farm layout optimization differential evolution genetic learning competitive elimination strategy genetic algorithm particle swarm optimization |
| title | A State-of-the-Art Fractional Order-Driven Differential Evolution for Wind Farm Layout Optimization |
| title_full | A State-of-the-Art Fractional Order-Driven Differential Evolution for Wind Farm Layout Optimization |
| title_fullStr | A State-of-the-Art Fractional Order-Driven Differential Evolution for Wind Farm Layout Optimization |
| title_full_unstemmed | A State-of-the-Art Fractional Order-Driven Differential Evolution for Wind Farm Layout Optimization |
| title_short | A State-of-the-Art Fractional Order-Driven Differential Evolution for Wind Farm Layout Optimization |
| title_sort | state of the art fractional order driven differential evolution for wind farm layout optimization |
| topic | sustainable energy wind farm layout optimization differential evolution genetic learning competitive elimination strategy genetic algorithm particle swarm optimization |
| url | https://www.mdpi.com/2227-7390/13/2/282 |
| work_keys_str_mv | AT sichentao astateoftheartfractionalorderdrivendifferentialevolutionforwindfarmlayoutoptimization AT sichengliu astateoftheartfractionalorderdrivendifferentialevolutionforwindfarmlayoutoptimization AT ruihanzhao astateoftheartfractionalorderdrivendifferentialevolutionforwindfarmlayoutoptimization AT yifeiyang astateoftheartfractionalorderdrivendifferentialevolutionforwindfarmlayoutoptimization AT hiroyoshitodo astateoftheartfractionalorderdrivendifferentialevolutionforwindfarmlayoutoptimization AT haichuanyang astateoftheartfractionalorderdrivendifferentialevolutionforwindfarmlayoutoptimization AT sichentao stateoftheartfractionalorderdrivendifferentialevolutionforwindfarmlayoutoptimization AT sichengliu stateoftheartfractionalorderdrivendifferentialevolutionforwindfarmlayoutoptimization AT ruihanzhao stateoftheartfractionalorderdrivendifferentialevolutionforwindfarmlayoutoptimization AT yifeiyang stateoftheartfractionalorderdrivendifferentialevolutionforwindfarmlayoutoptimization AT hiroyoshitodo stateoftheartfractionalorderdrivendifferentialevolutionforwindfarmlayoutoptimization AT haichuanyang stateoftheartfractionalorderdrivendifferentialevolutionforwindfarmlayoutoptimization |