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A parallel genetic algorithm for continuous and pattern-free heliostat field optimization

Cruz, N.C. and Salhi, S. and Redondo, J.L. and Alvarez, J.D. and Berenguel, M. and Ortigosa, P.M. (2017) A parallel genetic algorithm for continuous and pattern-free heliostat field optimization. In: Proceedings of the 17th International Conference on Computational and Mathematical Methods in Science and Engineering CMMSE-2017. , pp. 684-694. ISBN 978-84-617-8694-7. (KAR id:62470)

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The heliostat field of a solar power tower system, considering both its deployment cost and potential energy loss at operation, must be carefully designed. This procedure implies facing a complex continuous, constrained and large-scale optimization problem. Hence, its resolution is generally wrapped by extra distribution patterns or layouts with a reduced set of parameters. Griding the available surface is also an useful strategy. However, those approaches limit the degrees of freedom at optimization. In this context, the authors of this work are working on a new meta-heuristic for heliostat field opti- mization by directly addressing the underlying problem. Attention is also given to the benefits of modern High-Performance Computing (HPC) to allow a wider exploration of the search-space. Thus, a parallel genetic optimizer has been designed for direct heliostat field optimization. It relies on elitism, uniform crossover, static penalization of infeasible solutions and tournament selection.

Item Type: Book section
Additional information: Publication not found on alternative repository online. MW 21.8.18
Subjects: H Social Sciences
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Depositing User: Said Salhi
Date Deposited: 28 Jul 2017 13:42 UTC
Last Modified: 19 Sep 2023 15:03 UTC
Resource URI: (The current URI for this page, for reference purposes)
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