Hybrid Local Search Nelder Mead for optimizing WECs position

A combination of a local search with Nelder-Mead Simplex direct search is proposed to optimize the Wave Energy Converters positions

In order to address environmental concerns and meet growing energy demand the development of green energy technology has expanded tremendously. One of the most promising types of renewable energy is ocean wave energy. While there has been strong research in the development of this technology to date there remain a number of technical hurdles to overcome. This research explores a type of wave energy converter (WEC) called a buoy. This work models a power station as an array of fully submerged three-tether buoys. The target problem of this work is to place buoys in a size-constrained environment to maximise power output. We show that a hybrid method of stochastic local search combined with Nelder-Mead Simplex direct search performs better than previous search techniques. Meanwhile, The performance of the proposed method (LS-NM) is evaluated by four real wave sea states from the southern coast of Australia (Perth, Sydney, Adelaide and Tasmania) with a high granularity of wave direction is used. The position optimization results can be seen in the below-published papers.
1.Neshat, M., Alexander, B., Wagner, M., & Xia, Y. (2018, July). A detailed comparison of meta-heuristic methods for optimising wave energy converter placements. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 1318-1325). ACM.
2.Mehdi Neshat, Bradley Alexander, Nataliia Y. Sergiienko, and Markus Wagner. 2019. A hybrid evolutionary algorithm framework for optimising power take off and placements of wave energy converters. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '19), Manuel López-Ibáñez (Ed.). ACM, New York, NY, USA, 1293-1301. DOI: https://doi.org/10.1145/3321707.3321806