Adaptive Neuro-Surrogate-Based Optimisation Method for Wave Energy Converters Placement Optimisation

Design optimisation of a multi-mode wave energy converter

A wave energy converter (WEC) similar to the CETO system developed by Carnegie Clean Energy is considered for design optimisation. This WEC is able to absorb power from heave, surge and pitch motion modes, making the optimisation problem nontrivial. The WEC dynamics is simulated using the spectral-domain model taking into account hydrodynamic forces, viscous drag, and power take-off forces. The design parameters for optimisation include the buoy radius, buoy height, tether inclination angles, and control variables (damping and stiffness). The WEC design is optimised for the wave climate at Alb…

A Hybrid Cooperative Co-evolution Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters

Wave energy technologies have the potential to play a significant role in the supply of renewable energy on a world scale. One of the most promising designs for wave energy converters (WECs) are fully submerged buoys. In this work, we explore the optimisation of WEC arrays consisting of a three-tether buoy model called CETO. Such arrays can be optimised for total energy output by adjusting both the relative positions of buoys in farms and also the power-take-off (PTO) parameters for each buoy. The search space for these parameters is complex and multi-modal. Moreover, the evaluation of each pa…

Optimisation of Large Wave Farms using a Multi-strategy Evolutionary Framework

Wave energy is a fast-developing and promising renewable energy resource. The primary goal of this research is to maximise the total harnessed power of a large wave farm consisting of fully-submerged three-tether wave energy converters (WECs). Energy maximisation for large farms is a challenging search problem due to the costly calculations of the hydrodynamic interactions between WECs in a large wave farm and the high dimensionality of the search space. To address this problem, we propose a new hybrid multi-strategy evolutionary framework combining smart initialisation, binary population-base…

Differential Evolution and Bayesian Optimisation for Hyper-Parameter Selection in Mixed-Signal Neuromorphic Circuits Applied to UAV Obstacle Avoidance

The Lobula Giant Movement Detector (LGMD) is a an identified neuron of the locust that detects looming objects and triggers its escape responses. Understanding the neural principles and networks that lead to these fast and robust responses can lead to the design of efficient facilitate obstacle avoidance strategies in robotic applications. Here we present a neuromorphic spiking neural network model of the LGMD driven by the output of a neuromorphic Dynamic Vision Sensor (DVS), which has been optimised to produce robust and reliable responses in the face of the constraints and variability of it…

使用Kriging偏最小二乘法解决神经进化高维代理建模的初步步骤

Initial Steps Towards Tackling High-dimensional Surrogate Modeling for Neuroevolution Using Kriging Partial Least Squares

针对神经进化中人工神经网络训练计算量大且需高度人工配置的问题，提出使用Kriging偏最小二乘方法高效构建高维近似代理模型，填补了代理辅助进化算法在神经进化领域的研究空白。

使用有效集成协方差矩阵适应进化算法的混合波浪-风系统功率优化

Hybrid Wave-wind System Power Optimisation Using Effective Ensemble Covariance Matrix Adaptation Evolutionary Algorithm

本研究针对漂浮式混合风浪系统的复杂优化问题，提出结合协方差矩阵适应、新颖性搜索和离散化技术的有效集成优化（EEA）方法，在多个海域测试中显著提升了系统的平均功率输出和收敛率。