Binary Genetic Algorithm for optimizing the WECs position

Binary Genetic Algorithm for optimizing the Wave Energy Converters position

Renewable energy, such as ocean wave energy, plays a pivotal role in addressing the tremendous growth of global energy demand. It is expected that wave energy will be one of the fastest-growing energy resources in the next decade, offering an enormous potential source of sustainable energy. This research investigates the placement optimization of oscillating buoy-type wave energy converters (WEC). The design of a wave farm consisting of an array of fully submerged three-tether buoys is evaluated. In a wave farm, buoy positions have a notable impact on the farm's output. Optimizing the buoy positions is a challenging research problem because of very complex interactions (constructive and destructive) between buoys. The main purpose of this research is maximizing the power output of the farm through the placement of buoys in a size-constrained environment. This code proposes a binary Genetic Algorithm [1] for position optimization of WECs.
Acknowledgement:
The fitness function is programmed and modified by Dr. Nataliia Sergiienko (01/06/2018)
https://www.adelaide.edu.au/directory/nataliia.sergiienko

The applied binary Genetic Algorithm is implemented based on the below paper
[1] Sharp, C., & DuPont, B. (2018). Wave energy converter array optimization: A genetic algorithm approach and minimum separation distance study. Ocean Engineering, 163, 148-156.

All optimization results are reported by the below paper :
Neshat, M., Alexander, B., Sergiienko, N., & Wagner, M. (2019). A new insight into the Position Optimization of Wave Energy Converters by a Hybrid Local Search. arXiv preprint arXiv:1904.09599.