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Simulated annealing aided genetic algorithm for gene selection from microarray data

COMPUTERS IN BIOLOGY AND MEDICINE 2023年第1期158卷 106854-106854页

作者： Marjit, Shyam Bhattacharyya, Trinav Chatterjee, Bitanu Sarkar, Ram Indian Inst Informat Technol Guwahati Dept Comp Sci & Engn Gauhati 781015 Assam India Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 W Bengal India

In recent times, microarray gene expression datasets have gained significant popularity due to their usefulness to identify different types of cancer directly through bio-markers. These datasets possess a high gene-to-sample ratio and high dimensionality, with only a few genes functioning as bio-markers. Consequently, a significant amount of data is redundant, and it is essential to filter out important genes carefully. In this paper, we propose the Simulated Annealing aided Genetic algorithm (SAGA), a meta-heuristic approach to identify informative genes from high-dimensional datasets. SAGA utilizes a two-way mutation-based Simulated Annealing (SA) as well as Genetic algorithm (GA) to ensure a good trade-off between exploitation and exploration of the search space, respectively. The naive version of GA often gets stuck in a local optimum and depends on the initial population, leading to premature convergence. To address this, we have blended a clustering-based population generation with SA to distribute the initial population of GA over the entire feature space. To further enhance the performance, we reduce the initial search space by a score-based filter approach called the Mutually Informed Correlation Coefficient (MICC). The proposed method is evaluated on 6 microarray and 6 omics datasets. Comparison of SAGA with contemporary algorithms has shown that SAGA performs much better than its peers. Our code is available at https://***/shyammarjit/SAGA.

关键词： Feature selection Genetic algorithm Simulated annealing optimization algorithm Gene expression Microarray dataset

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Investigation in economic analysis of microgrids based on renewable energy uncertainty and demand response in the electricity market

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ENERGY 2021年 225卷 120247-120247页

作者： Huang, Shoujun Abedinia, Oveis Univ Sci & Technol China Sch Publ Affairs Hefei 230026 Peoples R China Nazarbayev Univ Dept Elec & Comp Engn Astana 010000 Kazakhstan

Owing to the increasing utilization of renewable resources such as wind turbines (WT), photovoltaic (PV) into a microgrid (MG), optimal planning has become important to satisfy the energy demand due to inherent uncertainties. This paper proposes a new model of planning based on renewable energy uncertainty and demand response and electric vehicles (EVs) in order to minimize the electricity market's total cost. Considering uncertainty challenges, energy storage system (ESS) and demand response programs based on time-of-use (TOU) are employed as a solution for managing the power flow in MG to warranty the essential load supporting and voltage stability and satisfy electrical and heat demands. Moreover, in this paper, the influence of price-based demand response (DR) for industrial, commercial, and residential loads is taken into account. Finally, the proposed problem is modeled as an optimization problem while the related decision variables are adjusted by a modified version of virus colony search (VCS) algorithm based on chaos theory in order to increase the exploitation and exploration terms. The proposed approach is tested on an MG system with several scenarios through analyzing the effect of DR programs based on the total cost reduction. As shown in the simulation results, DR highly reduced total cost (20-26% related to the case without DR), in which voltage dip (maximum 1.4%) and power deviation (maximum 1.2%) were enhanced. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Energy storage systems Demand side management Electricity market optimization algorithm Electric vehicle

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A Hybrid Method Based on Singular Spectrum Analysis, Firefly algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting

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ENERGIES 2016年第10期9卷 757页

作者： Gao, Yuyang Qu, Chao Zhang, Kequan Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Lanzhou Univ Key Lab Arid Climat Change & Reducing Disaster Ga Coll Atmospher Sci Lanzhou 730000 Peoples R China

With increasing importance being attached to big data mining, analysis, and forecasting in the field of wind energy, how to select an optimization model to improve the forecasting accuracy of the wind speed time series is not only an extremely challenging problem, but also a problem of concern for economic forecasting. The artificial intelligence model is widely used in forecasting and data processing, but the individual back-propagation artificial neural network cannot always satisfy the time series forecasting needs. Thus, a hybrid forecasting approach has been proposed in this study, which consists of data preprocessing, parameter optimization and a neural network for advancing the accuracy of short-term wind speed forecasting. According to the case study, in which the data are collected from Peng Lai, a city located in China, the simulation results indicate that the hybrid forecasting method yields better predictions compared to the individual BP, which indicates that the hybrid method exhibits stronger forecasting ability.

关键词： hybrid method short-term wind speed series forecasting forecasting accuracy neural network artificial intelligence optimization algorithm

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Evaluation of China's regional innovation capability based on simulated annealing projection pursuit model and nested fuzzy evaluation model

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EXPERT SYSTEMS 2023年第5期40卷 e13179-e13179页

作者： Ge, Mina Lin, Hualiang Zhejiang SHUREN Univ Sch Business Adm Hangzhou Peoples R China Tourism Coll Zhejiang Sch Travel & Serv Management Hangzhou 311231 Peoples R China

Regional innovation capability is an important indicator of both regional innovative and long-term development. The purpose of this study is to build an evaluation index system for regional innovation capability in order to identify regional differences and support innovation more effectively. After establishing a reasonable evaluation value for regional innovation capability, a combination of simulated annealing optimized projection pursuit (SA-PP) and N-layer nested fuzzy comprehensive evaluation models is used to assess China's regional innovation capabilities. The results show that the SA-PP model effectively mitigates the risk of erroneous evaluation results caused by index weight uncertainty, resulting in a more reasonable, robust, and intelligent assessment of regional innovation capability. Furthermore, the nested fuzzy comprehensive evaluation model is capable of easily resolving the evaluation factor set's heterogeneity and multilayer problems. The most significant influences on China's regional innovation capability are knowledge acquisition and enterprise innovation. The comprehensive score of the proposed combinational evaluation model manifests that provinces with strong regional innovation capabilities are mainly concentrated in the southeast coastal regions. The research results allow for precise weight determination and object ranking.

关键词： innovation capability intelligent evaluation nested fuzzy model optimization algorithm projection pursuit regional innovation simulated annealing

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EQUIVALENT PLATE ANALYSIS OF AIRCRAFT WING BOX STRUCTURES WITH GENERAL PLANFORM GEOMETRY

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JOURNAL OF AIRCRAFT 1986年第11期23卷 859-864页

作者： GILES, GL NASA Langley Research Center Hampton Virginia

Described is a new equivalent plate analysis formulation that is capable of modeling aircraft wing structures with a general-planform such as cranked wing boxes. Multiple trapezoidal segments are used to represent such planforms. A Ritz solution technique is used in conjunction with global displacement functions that encompass all the segments. This Ritz solution procedure is implemented efficiently into a computer program so that it can be used by rigorous optimization algorithms for application in early preliminary design. A direct method to interface this structural analysis procedure with aerodynamic programs for use in aeroelastic calculations is described. This equivalent plate analysis procedure is used to calculate the static deflections and stresses and vibration frequencies and modes of an example wing configuration. The numerical results are compared with results from a finite element model of the same configuration to illustrate typical levels of accuracy and computation times resulting from use of this procedure. © 1986 American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

关键词： Wing Box Structure Finite Element Analysis Wing Box Structural Response Aircraft Wings Computer Programs optimization algorithm Wing Planforms Structural Modeling Total Energy

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A new prediction method of seafloor hydrothermal active field based on wavelet neural network

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MARINE GEOPHYSICAL RESEARCH 2020年第4期41卷 19-19页

作者： Liu, Longlong Lu, Zichen Ma, Di Ma, Mingjiao Ocean Univ China Sch Math Sci Qingdao 266100 Peoples R China Chinese Acad Sci Cloud Comp Ctr Dongguan 523000 Peoples R China

The study of modern seafloor hydrothermal activity and its mineralization has become one of the focuses of global geoscience. Accurate prediction of possible seafloor hydrothermal active fields is the basis of all research work. The detecting method for new seafloor hydrothermal activity still is mainly dependent on marine site investigation. This includes a series of temperature, turbidity, and geochemical anomaly investigations using submarine cameras and manned diving investigation. Both of these require expensive financial, human, and material resources. In order to realize the accurate prediction of potential hydrothermal activity areas in a low cost manner, with strong pertinence, and a wide range, we propose a prediction method of seafloor hydrothermal active region based on Wavelet Neural Network. First, we integrated the hydrothermal position information from the InterRidge Vents Database with the hydrothermal temperature information form the Argo Database to construct a data set. Then, we combined wavelet analysis with an artificial neural network to create a wavelet neural network optimization algorithm. Finally, the temperature and salinity data were input to the wavelet neural network to predict the seafloor hydrothermal active region. Sevenfold cross validation was used to evaluate the performance of the model and 90.43% prediction accuracy was achieved. The results of experiments show that salinity is not related to the existence of hydrothermal activity fields but rather that the surrounding water temperature has a strong correlation with hydrothermal existence. Therefore, it is effectively feasible to use the wavelet neural network model with an input of seawater temperature to predict seafloor hydrothermal activity fields. Although artificial neural network cannot completely replace traditional hydrothermal exploration technology, it can provide a valuable reference with a strong target.

关键词： Seafloor hydrothermal activity field Detecting method Wavelet neural network model Prediction optimization algorithm

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Web information mining and semantic analysis in heterogeneous unstructured text data using enhanced latent Dirichlet allocation

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第1期35卷 e7410-e7410页

作者： Venugopal, Madamanchi Sharma, Virendra K. Sharma, Kalpana Bhagwant Univ Dept CSE Ajmer 305004 Rajasthan India Bhagwant Univ Dept EEE Ajmer Rajasthan India

Information mining and semantic analysis have gained significant attention over recent years to obtain appropriate information from unstructured data. Several approaches have been introduced for web information mining. However, the expected accuracy is not reached by these approaches. Therefore, hybrid fuzzy clustering and enhanced latent Dirichlet allocation (ELDA) are proposed for the accuracy increment in this work. The information clustering process is performed using the hybrid fuzzy clustering algorithm called fuzzy-C-medoids optimized using improved whale algorithm. The clustering procedure entails grouping data points into more similar clusters than data points from other clusters. Finally, the context of the text is recognized by analyzing the semantic information with ELDA, which offers a suitable index for accurate and fast data extraction. PYTHON tool is used to develop the proposed web mining model, and the simulation analysis of the proposed model is carried out using the BibTex dataset and compared with baseline models. The performance of the proposed method is evaluated using purity, normalized mutual information, accuracy, and precision metrics. Also, it is compared with different existing algorithms. Thus, the analysis of the results proved that the proposed approach achieves better outcomes than the existing approaches.

关键词： clustering optimization algorithm preprocessing semantic analysis text mining web mining

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Spare Aeroengine Demand Prediction Model Based on Deep Croston Method

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2020年第2期17卷 125-133页

作者： Liu, Lie Lin, Lin Li, Zhen Guo, Hao Lv, Yancheng Harbin Inst Technol Sch Mechatron Engn Harbin 150001 Peoples R China

The spare aeroengine demand prediction is a crucial issue in aeroengine fleet management. For more accurate predictions, a spare aeroengine demand prediction model is proposed based on the deep Croston method. The Croston framework is adopted to deal with the intermittent demand characteristic. The demand interval prediction model and the demand amount prediction model were developed by the long short-term memory deep learning network. Accompanied with the proposed spare aeroengine demand prediction model, a comprehensive evaluation method of intermittent demand prediction is proposed. The actual spare aeroengine demand data of an airline company were adopted to validate the proposed spare aeroengine demand prediction model. The traditional Croston method was regarded as the benchmark comparison model. Under the Croston framework, the machine learning methods, including back propagation neural network, support vector machine, gradient boosting decision tree, extreme gradient boosting tree, and multilayer perceptron network, were also adopted as the comparison models. The prediction results showed that the proposed spare aeroengine demand prediction model based on the deep Croston method achieved evident advantages. Compared with the traditional Croston method, the machine learning methods provide a satisfactory way for intermittent demand prediction.

关键词： Aeroengine Recurrent Neural Network Support Vector Machine Turbofan Stochastic Gradient Descent optimization algorithm Inventory Management Hydraulic Systems Civil Aircraft Flight Planning

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A Hybrid System Based on LSTM for Short-Term Power Load Forecasting

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ENERGIES 2020年第23期13卷 6241页

作者： Jin, Yu Guo, Honggang Wang, Jianzhou Song, Aiyi Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

As the basic guarantee for the reliability and economic operations of state grid corporations, power load prediction plays a vital role in power system management. To achieve the highest possible prediction accuracy, many scholars have been committed to building reliable load forecasting models. However, most studies ignore the necessity and importance of data preprocessing strategies, which may lead to poor prediction performance. Thus, to overcome the limitations in previous studies and further strengthen prediction performance, a novel short-term power load prediction system, VMD-BEGA-LSTM (VLG), integrating a data pretreatment strategy, advanced optimization technique, and deep learning structure, is developed in this paper. The prediction capability of the new system is evaluated through simulation experiments that employ the real power data of Queensland, New South Wales, and South Australia. The experimental results indicate that the developed system is significantly better than other comparative systems and shows excellent application potential.

关键词： power load forecasting hybrid analysis-forecast system data preprocessing strategy deep learning structure optimization algorithm

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Achieving wind power and photovoltaic power prediction: An intelligent prediction system based on a deep learning approach

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ENERGY 2023年 283卷

作者： Zhang, Yagang Pan, Zhiya Wang, Hui Wang, Jingchao Zhao, Zheng Wang, Fei North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewable Beijing 102206 Peoples R China North China Elect Power Univ Hebei Key Lab Phys & Energy Technol Box 205 Baoding 071003 Hebei Peoples R China North China Elect Power Univ Dept Automat Baoding 071003 Peoples R China North China Elect Power Univ Dept Elect Engn Baoding 071003 Peoples R China Univ South Carolina Interdisciplinary Math Inst Columbia SC 29208 USA

Accurately predicting wind and photovoltaic power is one of the keys to improving the economy of wind-solar complementary power generation system, reducing scheduling costs and no-load losses, and ensuring grid stability. However, the natural properties of energy result in complex fluctuations in their corresponding power sequences, making accurate predictions difficult. Therefore, this paper proposes an intelligent prediction system that combines decomposition algorithms and deep learning for ultra-short-term prediction of wind and photovoltaic power. First, an improved decomposition algorithm is proposed, based on fuzzy entropy's property that its value increases with the increase of sequence uncertainty, particle swarm optimization (PSO) is used to search for the optimal parameter combinations of variational modal decomposition (VMD), so that it can automatically adjust the parameters for energy data with different characteristics to reduce the human error. Then, a convolutional neural network (CNN) architecture that balances operational efficiency and prediction performance is constructed, and the hyperparameters of the CNN are optimized using the wild horse optimization algorithm (WHO) to improve the stability and accuracy of the prediction model. In this paper, real data from wind power plants and photovoltaic power plants in China are used as experimental objects, and experiments are carried out in three aspects, namely, benchmark model selection, decomposition algorithm comparison and combined model comparison. The results show that selecting CNN as the benchmark model is a good choice;the improved VMD has better decomposition performance than other state-of-the-art decomposition algorithms. The system proposed in this paper is highly generalizable and adaptive, and its prediction performance and accuracy greatly outperform that of other comparative models, with prediction accuracies improved by 72% and 79%, respectively, compared to a single CNN model.

关键词： Renewable energy Short-term forecast VMD CNN optimization algorithm

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