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 Heterogeneous comprehensive learning Particle Swarm Optimization [1] for the position optimization of WECs.

We would like to express our deep gratitude to Prof.Suganthan and Dr.Lynn for publishing the source code of HCLPSO.
[1] Lynn, N., & Suganthan, P. N. (2015). Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm and Evolutionary Computation, 24, 11-24. http://www.ntu.edu.sg/home/epnsugan

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.
https://www.researchgate.net/publication/332590555_A_new_insight_into_the_Position_Optimization_of_Wave_Energy_Converters_by_a_Hybrid_Local_Search