One of the most encouraging sorts of renewable energy is ocean wave energy. In spite of a large number of investigations in this field during the last decade, wave energy technologies are recognised as neither mature nor broadly commercialised compared to other renewable energy technologies. In this paper, we develop and optimise Power Take-off (PTO) configurations of a well-known wave energy converter (WEC) called a point absorber. This WEC is a fully submerged buoy with three tethers, which was proposed and developed by Carnegie Clean Energy Company in Australia. Optimising the WEC’s PTO parameters is a challenging engineering problem due to the high dimensionality and complexity of the search space. This research compares the performance of five state-of-the-art metaheuristics (including Covariance Matrix Adaptation Evolution Strategy, Gray Wolf optimiser, Harris Hawks optimisation, and Grasshopper Optimisation Algorithm) based on the real wave scenario in Sydney sea state. The experimental achievements show that the Multiverse optimisation (MVO) algorithm performs better than the other metaheuristics applied in this work.

中文翻译：

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用于波能转换器功率起飞优化的元启发式算法的比较研究：以澳大利亚东部为例

海浪能是最令人鼓舞的一种可再生能源。尽管在过去十年中在该领域进行了大量研究，但与其他可再生能源技术相比，波浪能技术尚未被公认为成熟或广泛商业化。在本文中，我们开发并优化了一种称为点吸收器的著名波能转换器（WEC）的取力器（PTO）配置。此WEC是由三个卡索完全浸没的浮标，由澳大利亚卡内基清洁能源公司（Carnegie Clean Energy Company）提出和开发。由于搜索空间的高维度和复杂性，优化WEC的PTO参数是一个具有挑战性的工程问题。这项研究比较了五种最新的元启发式方法（包括协方差矩阵适应进化策略，灰狼优化器，哈里斯·霍克斯优化算法和蚱hopper优化算法）基于悉尼海域的真实波浪情景。实验结果表明，Multiverse优化（MVO）算法的性能优于该工作中应用的其他元启发式算法。