Design of a parallel genetic algorithm for continuous and pattern-free heliostat field optimization

Cruz, N. C., Salhi, Said, Redondo, J. L., Álvarez, J. D., Berenguel, M., Ortigosa, P. M. (2019) Design of a parallel genetic algorithm for continuous and pattern-free heliostat field optimization. The Journal of Supercomputing, 75 (3). pp. 1268-1283. ISSN 0920-8542. (doi:10.1007/s11227-018-2404-8)

Abstract

The heliostat field of solar power tower plants can suppose up to 50% of investment costs and 40% of energy loss. Unfortunately, obtaining an optimal field requires facing a complex non-convex, continuous, large-scale, and constrained optimization problem. Although pattern-based layouts and iterative deployment are popular heuristics to simplify the problem, they limit flexibility and might be suboptimal. This work describes a new genetic algorithm for continuous and pattern-free heliostat field optimization. Considering the potential computational cost of the objective function and the necessity of broad explorations, it has been adapted to run in parallel on shared-memory environments. It relies on elitism, uniform crossover, static penalization of infeasibility, and tournament selection. Interesting experimental results show an optimization speedup up to 15× with 16 threads. It could approximately reduce a one year runtime, at complete optimization, to a month only. The optimizer has also been made available as a generic C++ library.

Item Type:	Article
DOI/Identification number:	10.1007/s11227-018-2404-8
Uncontrolled keywords:	Genetic algorithm, Parallel computing, Heliostat field optimization, Solar power tower
Divisions:	Faculties > Social Sciences > Kent Business School > Centre for Logistics and Heuristic Organisation (CLHO)
Depositing User:	Said Salhi
Date Deposited:	17 May 2018 08:22 UTC
Last Modified:	15 Jul 2019 07:59 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/67058 (The current URI for this page, for reference purposes)
Salhi, Said:	https://orcid.org/0000-0002-3384-5240