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Daily global solar radiation prediction based on a hybrid coral reefs optimization - Extreme Learning Machine approach

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SOLAR ENERGY 2014年 105卷 91-98页

作者： Salcedo-Sanz, S. Casanova-Mateo, C. Pastor-Sanchez, A. Sanchez-Giron, M. Univ Alcala de Henares Dept Signal Proc & Commun E-28801 Alcala De Henares Spain Univ Valladolid Dept Appl Phys Valladolid Spain

This paper discusses the performance of a novel coral reefs optimization Extreme Learning Machine (CRO-ELM) algorithm in a real problem of global solar radiation prediction. The work considers different meteorological data from the radiometric station at Murcia (southern Spain), both from measurements, radiosondes and meteorological models, and fully describes the hybrid CRO-ELM to solve the prediction of the daily global solar radiation from these data. The algorithm is designed in such a way that the ELM solves the prediction problem, whereas the CRO evolves the weights of the neural network, in order to improve the solutions obtained. The experiments carried out have shown that the CRO-ELM approach is able to obtain an accurate prediction of the daily global radiation, better than the classical ELM, and the Support Vector Regression algorithm. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Daily global solar radiation prediction coral reefs optimization algorithm Extreme Learning Machines

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Feature selection in wind speed prediction systems based on a hybrid coral reefs optimization - Extreme learning machine approach

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ENERGY CONVERSION AND MANAGEMENT 2014年 87卷 10-18页

作者： Salcedo-Sanz, S. Pastor-Sanchez, A. Prieto, L. Blanco-Aguilera, A. Garcia-Herrera, R. Univ Alcala de Henares Dept Signal Proc & Commun E-28801 Alcala De Henares Spain Iberdrola Dept Energy Resource Bilbao Spain Univ Complutense Madrid Dept Atmospher Phys E-28040 Madrid Spain Inst Geociencias UCM CSIC IGEO Madrid Spain

This paper presents a novel approach for short-term wind speed prediction based on a coral reefs optimization algorithm (CRO) and an Extreme Learning Machine (ELM), using meteorological predictive variables from a physical model (the Weather Research and Forecast model, WRF). The approach is based on a Feature Selection Problem (FSP) carried out with the CRO, that must obtain a reduced number of predictive variables out of the total available from the WRF. This set of features will be the input of an ELM, that finally provides the wind speed prediction. The CRO is a novel bio-inspired approach, based on the simulation of reef formation and coral reproduction, able to obtain excellent results in optimization problems. On the other hand, the ELM is a new paradigm in neural networks' training, that provides a robust and extremely fast training of the network. Together, these algorithms are able to successfully solve this problem of feature selection in short-term wind speed prediction. Experiments in a real wind farm in the USA show the excellent performance of the CRO-ELM approach in this FSP wind speed prediction problem. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Short term wind speed prediction Feature selection problem coral reefs optimization algorithm Extreme learning machines

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A CRO-species optimization scheme for robust global solar radiation statistical downscaling

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RENEWABLE ENERGY 2017年 111卷 63-76页

作者： Salcedo-Sanz, S. Jimenez-Fernandez, S. Aybar-Ruiz, A. Casanova-Mateo, C. Sanz-Justo, J. Garcia-Herrera, R. Univ Alcala Dept Signal Proc & Commun Alcala De Henares Spain Univ Valladolid LATUV Remote Sensing Lab Valladolid Spain Univ Politecn Madrid Dept Civil Engn Construct Infrastruct & Transport Madrid Spain Univ Complutense Madrid Dept Astrophys & Atmospher Sci Madrid Spain UCM CSIC Inst Geociencias IGEO Dept Sedimentary Geol & Environm Change Madrid Spain

This paper tackles the prediction of the global solar radiation (GSR) at a given point, using as predictive variables the outputs of a numerical weather model (the WRF meso-scale model) obtained at a different grid points. Prediction is obtained in this work using a Multilayer Perceptron (MLP) trained with Extreme Learning Machines (ELMS). Provided that the number of WRF outputs is vast, we propose the use of a coral reefs optimization algorithm with species (CRO-SP) to obtain a reduced number of significant predictive variables, therefore improving the global solar radiation prediction attained without feature selection. The proposed system has been tested on real data from a radiometric station located at Toledo (Spain) and average best results of RMSE of 69.19 W/m(2) have been achieved, resulting in a 21.62% improvement over the average prediction without considering the CRO-SP for the feature selection. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： coral reefs optimization algorithm CRO with species Global solar radiation Solar energy Extreme learning machines

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

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GLOBAL AND PLANETARY CHANGE 2019年第Jul.期178卷 15-34页

作者： Salcedo-Sanz, S. Garcia-Herrera, R. Camacho-Gomez, C. Alexandre, E. Carro-Calvo, L. Jaume-Santero, F. Univ Alcala Dept Signal Proc & Commun Madrid Spain Univ Complutense Madrid Dept Earth Phys & Astrophys Madrid Spain Inst Geociencias UCM CSIC IGEO Madrid Spain

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 ungridded 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 co

关键词： Most representative points Temperature fields reconstruction coral reefs optimization algorithm Analogue method

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Wind power field reconstruction from a reduced set of representative measuring points

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APPLIED ENERGY 2018年 228卷 1111-1121页

作者： Salcedo-Sanz, S. Garcia-Herrera, R. Camacho-Gomez, C. Aybar-Ruiz, A. Alexandre, E. Univ Alcala Dept Signal Proc & Commun Madrid Spain Univ Complutense Madrid Dept Earth Phys & Astrophys Madrid Spain UCM CSIC Inst Geociencias IGEO Madrid Spain

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 f

关键词： Wind power field reconstruction Wind resource analysis Representative points coral reefs optimization algorithm Analogue method

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Feature selection in machine learning prediction systems for renewable energy applications

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2018年 90卷 728-741页

作者： Salcedo-Sanz, S. Cornejo-Bueno, L. Prieto, L. Paredes, D. Garcia-Herrera, R. Univ Alcala Dept Signal Proc & Commun Madrid 28805 Spain Iberdrola Dept Energy Resource Bilbao Spain Univ Complutense Madrid Dept Earth Phys & Astrophys Madrid Spain CSIC IGEO Inst Geociencias UCM Madrid Spain

This paper focuses on feature selection problems that arise in renewable energy applications. Feature selection is an important problem in machine learning, both in classification and regression problems. In renewable energy systems, feature selection appears related to prediction systems in the most important sources such as wind, solar and marine resources. The objective of the paper is twofold: first, a review of the most important prediction systems for renewable energy applications involving feature selection is carried out. Analysis and discussion of different feature selection problems in prediction systems are considered. We show that wrapper FSP approaches are those mostly used due to their higher performance. They include a diversity of algorithms, prevailing fast-training approaches. The lack of an uniform framework for FSP and the diversity of tackled problems impede a systematic assessment of the performance and properties of the applied methods. Thus, the simultaneously use of several global search mechanisms should be the preferred option. In a second part of the paper, we explore this possibility, by introducing a novel approach for feature selection based on a novel meta-heuristic, the coral reefs optimization algorithm with Substrate Layer. This approach is able to combine different search mechanisms into a single algorithm, providing a global search procedure of high quality. We use an Extreme Learning Machine for prediction within this novel approach. The performance of the system is evaluated in a problem of wind speed prediction from numerical models input, using real data from a wind farm in Spain, where comparison with alternative regression algorithms is carried out. Improvements up to 20% in hourly and daily wind speed prediction are obtained with the proposed system versus the algorithms without the feature selection process considered.

关键词： Renewable energy Feature selection problems Prediction problems coral reefs optimization algorithm Extreme learning machines

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The explosion operation of fireworks algorithm boosts the coral reef optimization for multimodal medical image registration

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 102卷 104252-104252页

作者： Chen, Yilin He, Fazhi Zeng, Xiantao Li, Haoran Liang, Yaqian Wuhan Univ Sch Comp Sci Wuhan Hubei Peoples R China Wuhan Inst Technol Sch Comp Sci & Engn Hubei Key Lab Intelligent Robot Wuhan Hubei Peoples R China Wuhan Univ Zhongnan Hosp Ctr Evidence Based & Translat Med Wuhan Hubei Peoples R China

Medical image registration is becoming increasingly important in diagnosis and treatment planning, as it enables to align and integrate different images having a shared content, which obtained under different conditions, into a single representation. The most challenging issues of registration is finding a optimal transformation which settled by optimization methods. With this work, we present a Fireworks algorithm (FWA) boosts the coral reefs optimization algorithm (CRO) for medical image registration (FWBCR). Firstly, the coral reefs optimization is easily trapping in local optimum, we enhance the exploration ability of the CRO by integrated with a explosion operator of the FWA. At the Broadcast Spawning stage of CRO, a large fraction of corals are applying the crossover operator which has a good exploitation ability and a small fraction of corals with good solutions are applying the explosion operator which has a good exploration ability. Therefore, the algorithm we proposed is well solved the trades off between exploitation and exploration. Secondly, the explosion amplitude affects the search ability. A larger explosion amplitude benefit for the exploration in the early stages of the algorithm. Contrary, a smaller explosion amplitude benefit for the exploitation in the late stages of the algorithm. To deal with these problems, we design an adaptive explosion amplitude which depends on the fitness of the best solution and the worst solution. Thirdly, the phenomenon of the explosion operator is to generate lots of sparks. It is hard to select a small number of sparks with less computation effort while to guarantee the diversity of the swarm for next generation from a large number of sparks. To overcome this difficulty, we exploring the use of the clustering algorithm on the sparks then get some cluster centers as the new solutions. In this way, the diversity of the swarm can be ensured and the computation effort can be greatly reduced through reducing the number o

关键词： Fireworks algorithm coral reefs optimization algorithm Swarm intelligence Image registration Medical imaging

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A hybrid deep learning approach with genetic and coral reefs metaheuristics for enhanced defect detection in software

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International Journal of Information Management Data Insights 2022年第2期2卷

作者： Tameswar, Kajal Suddul, Geerish Dookhitram, Kumar University of Technology (UTM) Pointe-aux-Sables Mauritius

Early detection and correction of software defects is essential in the software development process. In the production stage, software with defects negatively impacts on operational costs and ultimately affects customer satisfaction. Although different approaches exist to predict software defects, two essential factors are timely and accurate detection. This paper presents a hybrid Deep Neural Network model for enhanced prediction of software bugs. Different Nature-Inspired algorithms have been applied to improve the exploration of the hyperparameter solution space to optimize the Deep Neural Network architecture. Experimental investigations have been conducted using NASA dataset to predict software defects and evaluation measures like accuracy, computational time and F1 score have been used for performance comparison. The approach based on the combination of Genetic and coral Reef metaheuristics outperformed all other models, achieving highest accuracy of around 96% and average F1-score of 0.92. © 2022 The Author(s)

关键词： coral reefs optimization algorithm Deep learning Genetic algorithm Metaheuristic algorithms Nature inspired optimization techniques Neural networks Software bug prediction

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