A parallel Teaching–Learning-Based Optimization procedure for automatic heliostat aiming

Cruz, Nicolás C.; Redondo, Juana López; Álvarez, José Domingo; Berenguel, Manuel; Ortigosa, Pilar M.

doi:10.1007/s11227-016-1914-5

Cited by 24 publications

(15 citation statements)

References 15 publications

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“…However in [14], the authors' parallel proposal for the particular problem under study, also achieves very good efficiencies, the cost function having a high computational cost. In Table 8, we compare the method proposed in [14] to both proposed methods, SPG_TLBO and SPP_TLBO, for the first function of the benchmark test (provided by the reference software in https://gitlab.hpca.ual.es/ncc911/ ParallelTLBO), i.e., the Sphere function, using between 2 to 10 threads (NoT), i.e., OpenMP processes. Results presented in Table 8 were obtained by running the reference code on the same parallel platform where the results for the SPG_TLBO and SPP_TLBO algorithms have been obtained.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Average speed-up values of 4.9x and 6.4x with 16 and 32 processors were obtained respectively, corresponding to efficiencies of 30% and 20% respectively. In [14], the authors propose a parallel TLBO procedure for automatic heliostat aiming, obtaining good speed-up values for this extremely expensive problem using up to 32 processes; parallel performance, however, worsened when using functions that were not so computationally expensive.…”

Section: Introductionmentioning

confidence: 99%

“…Algorithm 7 shows the parallel learner phase for the SPP_ParTLBO algorithm. As can be seen, after identifying the best individual (i.e., the teacher) the thread that stored it in its subpopulation copies it into the global memory, so all the threads use the same teacher (lines [11][12][13][14][15][16][17].…”

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confidence: 99%

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Efficient Subpopulation Based Parallel TLBO Optimization Algorithms

et al. 2018

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A numerous group of optimization algorithms based on heuristic techniques have been proposed in recent years. Most of them are based on phenomena in nature and require the correct tuning of some parameters, which are specific to the algorithm. Heuristic algorithms allow problems to be solved more quickly than deterministic methods. The computational time required to obtain the optimum (or near optimum) value of a cost function is a critical aspect of scientific applications in countless fields of knowledge. Therefore, we proposed efficient algorithms parallel to Teaching-learning-based optimization algorithms. TLBO is efficient and free from specific parameters to be tuned. The parallel proposals were designed with two levels of parallelization, one for shared memory platforms and the other for distributed memory platforms, obtaining good parallel performance in both types of parallel architectures and on heterogeneous memory parallel platforms.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient Subpopulation Based Parallel TLBO Optimization Algorithms

et al. 2018

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“…The Jaya optimization algorithm and the three new Rao's optimization algorithms (i.e., RAO1, RAO2, and RAO3) are described in Algorithm 1. The Jaya optimization algorithm has been successfully used for solving a large number of large-scale industrial problems [53][54][55][56][57][58][59]. The three new Rao's optimization algorithms are metaphor-less algorithms based on the best and worst solutions obtained during the optimization process and the random interactions between the candidate solutions [60][61][62].…”

Section: Preliminariesmentioning

confidence: 99%

Settings-Free Hybrid Metaheuristic General Optimization Methods

et al. 2020

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Several population-based metaheuristic optimization algorithms have been proposed in the last decades, none of which are able either to outperform all existing algorithms or to solve all optimization problems according to the No Free Lunch (NFL) theorem. Many of these algorithms behave effectively, under a correct setting of the control parameter(s), when solving different engineering problems. The optimization behavior of these algorithms is boosted by applying various strategies, which include the hybridization technique and the use of chaotic maps instead of the pseudo-random number generators (PRNGs). The hybrid algorithms are suitable for a large number of engineering applications in which they behave more effectively than the thoroughbred optimization algorithms. However, they increase the difficulty of correctly setting control parameters, and sometimes they are designed to solve particular problems. This paper presents three hybridizations dubbed HYBPOP, HYBSUBPOP, and HYBIND of up to seven algorithms free of control parameters. Each hybrid proposal uses a different strategy to switch the algorithm charged with generating each new individual. These algorithms are Jaya, sine cosine algorithm (SCA), Rao’s algorithms, teaching-learning-based optimization (TLBO), and chaotic Jaya. The experimental results show that the proposed algorithms perform better than the original algorithms, which implies the optimal use of these algorithms according to the problem to be solved. One more advantage of the hybrid algorithms is that no prior process of control parameter tuning is needed.

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“…It is no longer necessary to store and/or access the "real" data and the meta-model can be used, i.e., deployed, where required. For instance, this approach has successfully been used to test automatic heliostat aiming strategies in [34].…”

Section: Model Deployment (Md)mentioning

confidence: 99%

A New Methodology for Building-Up a Robust Model for Heliostat Field Flux Characterization

et al. 2017

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Abstract:The heliostat field of solar central receiver systems (SCRS) is formed by hundreds, even thousands, of working heliostats. Their adequate configuration and control define a currently active research line. For instance, automatic aiming methodologies of existing heliostat fields are being widely studied. In general, control techniques require a model of the system to be controlled in order to obtain an estimation of its states. However, this kind of information may not be available or may be hard to obtain for every plant to be studied. In this work, an innovative methodology for data-based analytical heliostat field characterization is proposed and described. It formalizes the way in which the behavior of a whole field can be derived from the study of its more descriptive parts. By successfully applying this procedure, the instantaneous behavior of a field could be expressed by a reduced set of expressions that can be seen as a field descriptor. It is not intended to replace real experimentation but to enhance researchers' autonomy to build their own reliable and portable synthetic datasets at preliminary stages of their work. The methodology proposed in this paper is successfully applied to a virtual field. Only 30 heliostats out of 541 were studied to characterize the whole field. For the validation set, the average difference in power between the flux maps directly fitted from the measured information and the estimated ones is only of 0.67% (just 0.10946 kW/m 2 of root-mean-square error, on average, between them). According to these results, a consistent field descriptor can be built by applying the proposed methodology, which is hence ready for use.

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A parallel Teaching–Learning-Based Optimization procedure for automatic heliostat aiming

Cited by 24 publications

References 15 publications

Efficient Subpopulation Based Parallel TLBO Optimization Algorithms

Efficient Subpopulation Based Parallel TLBO Optimization Algorithms

Settings-Free Hybrid Metaheuristic General Optimization Methods

A New Methodology for Building-Up a Robust Model for Heliostat Field Flux Characterization

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