Surrogate-Assisted Differential Evolution for Wave Energy Converters Optimization

Zihang Zhang, Zhiming Zhang, Zhenyu Lei, Runqun Xiong*, Jiujun Cheng*, Shangce Gao*

*この論文の責任著者

研究成果: ジャーナルへの寄稿学術論文査読

2 被引用数 (Scopus)

抄録

In the aftermath of the successes in wind and solar energy, wave energy has emerged as an exceptionally promising renewable resource. A pivotal aspect of wave energy development involves optimizing the placement of energy converters in constrained sea areas to achieve maximum output. However, the hydrodynamic interactions among buoys in wave energy arrays pose a formidable challenge, making power assessment modeling both expensive and intricate. This study introduces a surrogate-assisted chaotic differential evolution (SDE) algorithm to address this challenge. The proposed methodology replaces computationally demanding evaluations with trained neural network models. Significantly, in contrast to prior surrogate models, we employ a selection of elite individuals to guide the evolutionary algorithm's search direction through confidence ranking. We curated eight datasets from four real wave scenarios (Perth, Adelaide, Tasmania, and Sydney) with two buoy configurations (4 and 16 buoys). Subsequently, we trained five state-of-the-art deep network models and two numerical regression models to identify the optimal model. Experimental results demonstrate that SDE exhibits superior energy output and computational efficiency compared to the other four intelligent algorithms when optimizing the locations of a highly challenging 16-buoy wave energy converter array in authentic wave environments.

本文言語英語
ジャーナルIEEE Transactions on Emerging Topics in Computational Intelligence
DOI
出版ステータス受理済み/印刷中 - 2024

ASJC Scopus 主題領域

  • コンピュータ サイエンスの応用
  • 制御と最適化
  • 計算数学
  • 人工知能

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