@article{nokey,

title = {Eliminating Stick-Slip Vibrations in Drill-Strings with a Dual-Loop Control Strategy Optimised by the CRO-SL Algorithm},

author = {Jorge Pérez-Aracil and C. Camacho-Gómez and Emiliano Pereira and Vahid Vaziri and Sumeet S Aphale and Sancho Salcedo-Sanz},

doi = {10.3390/math9131526},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Mathematics},

volume = {8},

number = {13},

keywords = {Coral Reefs Optimization},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Design of a Multi-Band Microstrip Textile Patch Antenna for LTE and 5G Services with the CRO-SL ensemble},

author = {C. Camacho-Gómez and Rocío Sánchez Montero and Diego Martínez Villanueva and Pablo Luís López Espí and Sancho Salcedo-Sanz},

doi = {app10031168},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {Applied Sciences},

volume = {10},

number = {3},

keywords = {Coral Reefs Optimization},

pubstate = {published},

tppubtype = {article}

}

@article{SALCEDOSANZ201915,

title = {Near-optimal selection of representative measuring points for robust temperature field reconstruction with the CRO-SL and analogue methods},

author = {Sancho Salcedo-Sanz and R. García-Herrera and C. Camacho-Gómez and E. Alexandre and L. Carro-Calvo and F. Jaume-Santero},

url = {https://www.sciencedirect.com/science/article/pii/S0921818118306039},

doi = {https://doi.org/10.1016/j.gloplacha.2019.04.013},

issn = {0921-8181},

year  = {2019},

date = {2019-01-01},

urldate = {2019-01-01},

journal = {Global and Planetary Change},

volume = {178},

pages = {15-34},

abstract = {In this paper we tackle a problem of representative measuring points selection for temperature field reconstruction. This problem is a version of the more general Representative Selection (RS) problem, well-known in computer and data science. In this particular case, the objective is to select the best set of N measuring points (i.e. N representative points), in such a way that a reconstruction error is minimized when reconstructing the monthly average temperature field. We use a novel meta-heuristic algorithm, the Coral Reefs Optimization with Substrate Layer (CRO-SL), which is an evolutionary-type method able to combine several different search procedures within a single population. The CRO-SL is combined with the Analogue Method (AM) to identify the most representative points. This approach exhibits strong performance from experiments with gridded and un-gridded temperature field datasets (European Climate Assessment & Dataset (ECA) and ERA-Interim reanalysis (ERA)). Different aspects such as the error assessment and the comparison with alternative approaches, are discussed in the experimental analysis of this article. We show that the algorithm performs better than a greedy approach, i.e. the best solution for N points is different from the N best individual predictors. The solutions obtained with the proposed methodology are climatologically consistent and include points from Scandinavia, Central and Southern Europe, the Black Sea and Central and South Western Asia as the more representative in the case of the ECA dataset; similar areas are selected for ERA. We have found out that once the number of stations/points goes over a threshold, the improvement in the model is obtained by increasing the density of data in the given zones, instead of adding data from different zones to the algorithm. The method proposed may have direct application in Palaeoclimalogy, where there are a large amount of distributed proxies with scarce information, so the proposed approach could be useful to select the most important ones to reconstruct a desired field.},

keywords = {Analogue method, Coral Reefs Optimization, Most representative points, Temperature fields reconstruction},

pubstate = {published},

tppubtype = {article}

}

@article{SALCEDOSANZ201879,

title = {An efficient neuro-evolutionary hybrid modelling mechanism for the estimation of daily global solar radiation in the Sunshine State of Australia},

author = {Sancho Salcedo-Sanz and Ravinesh C. Deo and Laura Cornejo-Bueno and C. Camacho-Gómez and Sujan Ghimire},

url = {https://www.sciencedirect.com/science/article/pii/S0306261917314976},

doi = {https://doi.org/10.1016/j.apenergy.2017.10.076},

issn = {0306-2619},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Applied Energy},

volume = {209},

pages = {79-94},

abstract = {This research paper aims to develop a hybrid neuro-evolutionary wrapper-based model for daily global solar radiation estimation in the solar-rich Sunshine State of Queensland, Australia. To design a robust hybrid modelling mechanism, the Interim-ERA European Centre for Medium Range Weather Forecasting (ECMWF) Reanalysis data are employed to train and cross-validate the estimation model that is formulated by an evolutionary-type algorithm: the Coral Reefs Optimization (CRO) integrated with an Extreme Learning Machine (ELM) model. The hybrid CRO-(ELM) algorithm is applied in two stages: first for the feature selection process guided by an ELM algorithm (a class of fast training neural network tool) as the model’s fitness function to screen an optimal set of predictor variables and second, for the estimation of the solar radiation using the optimally screened variables by the final hybrid CRO-(ELM)-ELM system. To benchmark the performance of the hybrid CRO-ELM algorithm for this estimation problem we apply an alternative, yet a similar feature screening approach: the Grouping Genetic Algorithm encoded into the ELM-based model (GGA-(ELM) used as the predictor mechanism). After the feature selection process is performed by the CRO algorithm that extracts the data patterns for accurate estimation the alternative objective algorithm is applied (in this case the ELM again) to formulate the hybrid CRO-(ELM)-ELM modelling system. To provide a rigorous evaluation of the CRO-(ELM)-ELM hybrid system, alternative estimation approaches are considered: the Multivariate Adaptive Regression Splines (MARS), Multiple Linear Regression (MLR) and the Support Vector Regression (SVR). The hybrid CRO-(ELM)-ELM system is tested in a real problem where the results are evaluated by means of several statistical score metrics and diagnostic plots of the observed and the estimated daily global solar radiation in the testing phase. We show that the hybrid CRO-(ELM)-ELM model is able to yield promising results; thus improving those attained by the 7 alternative models (i.e., hybrid CRO-(ELM)-MARS, CRO-(ELM)-MLR and CRO-(ELM)-SVR and the GGA equivalent models). The study ascertains that the CRO-based hybrid system where a large pool of predictor data are carefully screened through a wrapper-based modelling system and the ELM model is applied as a objective estimation tool can be adopted as a qualified stratagem in solar radiation estimation problems. The proposed hybrid CRO-(ELM)-ELM system exhibits clear advantages over the alternative machine learning approaches tested and also over the other machine learning algorithms used without the feature selection tool; thus advocating its scientific utility in renewable energy applications.},

keywords = {Coral Reefs Optimization, Extreme Learning Machines, Grouping Genetic Algorithms, Hybrid modelling system, Renewable Energies, Solar radiation estimation},

pubstate = {published},

tppubtype = {article}

}

@article{CAMACHOGOMEZ201814,

title = {Active vibration control design using the Coral Reefs Optimization with Substrate Layer algorithm},

author = {C. Camacho-Gómez and X. Wang and E. Pereira and I. M. Díaz and Sancho Salcedo-Sanz},

url = {https://www.sciencedirect.com/science/article/pii/S0141029617311719},

doi = {https://doi.org/10.1016/j.engstruct.2017.12.002},

issn = {0141-0296},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Engineering Structures},

volume = {157},

pages = {14-26},

abstract = {Active vibration control (AVC) via inertial-mass actuators is a viable technique to mitigate human-induced vibrations in civil structures. A multi-input multi-output (MIMO) AVC has been previously proposed in the literature to simultaneously find the sensor/actuator pairs’ optimal placements and tune the control gains. However, the method involved local gradient-based methods, which is not affordable when the number of possible locations of actuators is large. In this case, the computation time to obtain a local solution may be huge and unaffordable, which limits the number of test points and/or actuators/sensors considered. This paper proposes an alternative approach based on a recently proposed meta-heuristic, the Coral Reefs Optimization (CRO) algorithm. More concretely, an enhanced version of the CRO is considered, the Coral Reefs Optimization with Substrate Layer (CRO-SL). The CRO-SL is a competitive co-evolution algorithm in which different exploration procedures are jointly evolved within a single population of potential solutions to the problem. The proposed algorithm is thus able to promote competition among different search methods to solve hard optimization problems. In terms of structural design, this work provides an important step to improve the applicability of AVC systems to real complex structures (with a large number of vibration modes and/or with a large number of test points) by achieving global optimum designs with affordable computation time. A finite element model of a real complex floor structure is used to illustrate the contributions of this paper.},

keywords = {Active vibration control, Bio-inspired Metaheuristics, Co-evolution, Coral Reefs Optimization, Human-induced vibrations, MIMO control, Single-objective Optimization},

pubstate = {published},

tppubtype = {article}

}

@article{SALCEDOSANZ20181111,

title = {Wind power field reconstruction from a reduced set of representative measuring points},

author = {Sancho Salcedo-Sanz and R. García-Herrera and C. Camacho-Gómez and A. Aybar-Ruíz and E. Alexandre},

url = {https://www.sciencedirect.com/science/article/pii/S0306261918310304},

doi = {https://doi.org/10.1016/j.apenergy.2018.07.003},

issn = {0306-2619},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

journal = {Applied Energy},

volume = {228},

pages = {1111-1121},

abstract = {In this paper we deal with a problem of representative measuring points selection for long-term wind power analysis. It has direct applications such as wind farm prospective location or long-term power generation prediction in wind-based energy facilities. The problem’s objective is to select the best set of N measuring points (i.e. N representative points), in such a way that a wind power error reconstruction measure is minimized, considering a monthly average wind power field. In order to solve this problem, we use a novel meta-heuristic algorithm, the Coral Reefs Optimization with Substrate Layer, which is an evolutionary-type method able to combine different search procedures within a single population. The CRO-SL is hybridized with the Analogue Method as wind power reconstruction method, to identify the most representative points for the wind field. The proposed approach has been tested in the reconstruction of monthly average wind power fields in Europe, from reanalysis data (ERA-Interim reanalysis). The method exhibits strong performance as evidenced from the experiments carried out. The solutions obtained show that the more significant measuring points are mainly located over the Atlantic ocean, which is consistent with the wind speed climatology of the Northern hemisphere mid-latitudes. We have also analyzed the set of least representative points to reconstruct the wind power field (less informative points for whole reconstruction of the field), obtaining points mainly located at the North of Scandinavia (which may be associated with the circumpolar circulation), and some points in the Eastern Mediterranean, which seem to be related to the Etesian winds. Reconstructions at seasonal scales show similar results, which provides confidence on the robustness of the proposed method. The proposed methodology can be further applied to alternative energy-related problems, such as the selection of critical energy infra-structures or the selection of critical points for climate change studies, among others.},

keywords = {Analogue method, Coral Reefs Optimization, Representative points, Wind power field reconstruction, Wind resource analysis},

pubstate = {published},

tppubtype = {article}

}

@article{SALCEDOSANZ201762,

title = {Structures vibration control via Tuned Mass Dampers using a co-evolution Coral Reefs Optimization algorithm},

author = {Sancho Salcedo-Sanz and C. Camacho-Gómez and A. Magdaleno and E. Pereira and A. Lorenzana},

url = {https://www.sciencedirect.com/science/article/pii/S0022460X17300391},

doi = {https://doi.org/10.1016/j.jsv.2017.01.019},

issn = {0022-460X},

year  = {2017},

date = {2017-01-01},

urldate = {2017-01-01},

journal = {Journal of Sound and Vibration},

volume = {393},

pages = {62-75},

abstract = {In this paper we tackle a problem of optimal design and location of Tuned Mass Dampers (TMDs) for structures subjected to earthquake ground motions, using a novel meta-heuristic algorithm. Specifically, the Coral Reefs Optimization (CRO) with Substrate Layer (CRO-SL) is proposed as a competitive co-evolution algorithm with different exploration procedures within a single population of solutions. The proposed approach is able to solve the TMD design and location problem, by exploiting the combination of different types of searching mechanisms. This promotes a powerful evolutionary-like algorithm for optimization problems, which is shown to be very effective in this particular problem of TMDs tuning. The proposed algorithm's performance has been evaluated and compared with several reference algorithms in two building models with two and four floors, respectively.},

keywords = {Bio-inspired Metaheuristics, Co-evolution, Coral Reefs Optimization, Structural vibration control, Tuned Mass Dampers},

pubstate = {published},

tppubtype = {article}

}