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      Randomising block sizes for BlockCopy-based wind farm layout optimisation

      Mayo, Michael; Daoud, Maisa; Zheng, Chen
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      IES paper.pdf
      Accepted version, 382.4Kb
      DOI
       10.1007/978-3-319-49049-6_20
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      Mayo, M., Daoud, M., & Zheng, C. (2016). Randomising block sizes for BlockCopy-based wind farm layout optimisation. In G. Leu, H. K. Singh, & S. Elsayed (Eds.), Proceedings of the 20th Asia Pacific Symposium on Intelligent and Evolutionary Systems, Canberra, Australia, November 2016 (pp. 277–289). Cham, Switzerland: Springer International Publishing AG. http://doi.org/10.1007/978-3-319-49049-6_20
      Permanent Research Commons link: https://hdl.handle.net/10289/10779
      Abstract
      The BlockCopy stochastic local search algorithm is a state-of-the-art optimiser for the Wind Farm Layout Optimisation problem. Unlike many other metaheuristics-based optimisers, BlockCopy requires the specification of only one key parameter, namely a block size. In this paper, we investigate the effect on different block sizes on the optimisation results. Using standard benchmarks for the Wind Farm Layout Optimisation problem, we show that smaller fixed block sizes (relative to overall layout size) produce better optimised layouts than larger fixed block sizes. More interestingly, we also show that randomising the block size parameter results in optimisation performance at the same or a better level than that produced by the best algorithm with a fixed block size. Effectively, this means that the user can ignore the need to tune the block size parameter and simply randomise it instead. Such a strategy results in what is effectively a parameterless, but none-the-less effective, optimisation algorithm for the Wind Farm Layout Optimisation problem.
      Date
      2016
      Type
      Conference Contribution
      Publisher
      Springer International Publishing AG
      Rights
      This is an author’s accepted version of an article published in Proceedings of the 20th Asia Pacific Symposium on Intelligent and Evolutionary Systems. © 2016 Springer International Publishing. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-49049-6_20
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      • Computing and Mathematical Sciences Papers [1454]
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