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Zhang X, Liu Q, Qu Y. An adaptive differential evolution algorithm with population size reduction strategy for unconstrained optimization problem. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
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2
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An adaptive mutation strategy correction framework for differential evolution. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08291-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
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3
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Multi population-based chaotic differential evolution for multi-modal and multi-objective optimization problems. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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4
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Wang M, Ma Y, Wang P. Parameter and strategy adaptive differential evolution algorithm based on accompanying evolution. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.06.040] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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5
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Multi-strategy adaptive cuckoo search algorithm for numerical optimization. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10222-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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6
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Sun Y, Wu F. DESAC: differential evolution sample consensus algorithm for image registration. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03266-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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7
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Gonçalves EN, Belo MAR, Batista AP. Self-adaptive multi-objective differential evolution algorithm with first front elitism for optimizing network usage in networked control systems. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2021.108112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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8
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Optimal Rescheduling of Generators to Alleviate Congestion in Transmission System: A Novel Modified Whale Optimization Approach. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-021-06136-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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9
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Zeng Z, Zhang M, Chen T, Hong Z. A new selection operator for differential evolution algorithm. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107150] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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10
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Liu H, Lu J, Zhao X, Xu T. Ionic liquids immobilized on nanomaterials: An efficient strategy in catalytic reactions. SYNTHETIC COMMUN 2021. [DOI: 10.1080/00397911.2021.1936057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Haitao Liu
- School of Petrochemical Engineering, Shenyang University of Technology, Liaoyang, China
| | - Jingjing Lu
- School of Petrochemical Engineering, Shenyang University of Technology, Liaoyang, China
| | - Xiudan Zhao
- School of Petrochemical Engineering, Shenyang University of Technology, Liaoyang, China
| | - Tiejun Xu
- School of Petrochemical Engineering, Shenyang University of Technology, Liaoyang, China
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11
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Duan R, Sun P. Basketball sports neural network model based on nonlinear classification. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-189431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
With the continuous innovation of science and technology, the mathematical modeling and analysis of bodily injury in the process of exercise have always been a hot and difficult point in the research field of scholars. Although there are many research results on the nonlinear classification of the basketball sports neural network model, usually only one model is used, which has certain defects. The combination forecasting model based on the ARIMA model and neural network based on LSTM can make up for this defect. In the process of the experiment, the most important is the construction of the combination model and the acquisition of volunteer data in the process of the ball game. In this experiment, the ARIMA model is used as the linear part of the data, and LSTM neural network model is used to get the sequence of body injury. The results of the empirical study show that: it is reasonable to divide the injury of thigh and calf in the process of basketball sports, which is very consistent with the force point of the human body in the process of sports. The results of the two models predicting the average degree of bodily injury for many times are about 0.32 and 0.38 respectively, which are far less than 1. The execution time of the program for simultaneous prediction on the computer is about 1 minute, which is extremely effective.
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Affiliation(s)
- Rongkai Duan
- College of Physical Education and Sports, Beijing Normal University, Beijing, China
| | - Pu Sun
- College of Physical Education and Sports, Beijing Normal University, Beijing, China
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12
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Suggesting a Stochastic Fractal Search Paradigm in Combination with Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings. ENERGIES 2021. [DOI: 10.3390/en14061649] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings’ energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this study is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS–ANNs). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA) are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model’s optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90% correlation) can adequately optimize the ANN. In this regard, this tool’s prediction error declined by nearly 23%, 18%, and 36% by applying the GOA, FA, and SFS techniques. Moreover, all used accuracy criteria indicated the superiority of the SFS over the benchmark schemes. Therefore, it is inferred that utilizing the SFS along with ANN provides a reliable hybrid model for the early prediction of CL.
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Affiliation(s)
| | | | - Mosstafa Kazemi
- Young Researchers and Elite Club, Ilam Branch, Islamic Azad University, Ilam, Iran
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14
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Zeng W, Yuan J, Yuan C, Wang Q, Liu F, Wang Y. A novel technique for the detection of myocardial dysfunction using ECG signals based on hybrid signal processing and neural networks. Soft comput 2021. [DOI: 10.1007/s00500-020-05465-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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15
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Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings. ENERGIES 2021. [DOI: 10.3390/en14051331] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
A reliable prediction of sustainable energy consumption is key for designing environmentally friendly buildings. In this study, three novel hybrid intelligent methods, namely the grasshopper optimization algorithm (GOA), wind-driven optimization (WDO), and biogeography-based optimization (BBO), are employed to optimize the multitarget prediction of heating loads (HLs) and cooling loads (CLs) in the heating, ventilation and air conditioning (HVAC) systems. Concerning the optimization of the applied algorithms, a series of swarm-based iterations are performed, and the best structure is proposed for each model. The GOA, WDO, and BBO algorithms are mixed with a class of feedforward artificial neural networks (ANNs), which is called a multi-layer perceptron (MLP) to predict the HL and CL. According to the sensitivity analysis, the WDO with swarm size = 500 proposes the most-fitted ANN. The proposed WDO-ANN provided an accurate prediction in terms of heating load (training (R2 correlation = 0.977 and RMSE error = 0.183) and testing (R2 correlation = 0.973 and RMSE error = 0.190)) and yielded the best-fitted prediction in terms of cooling load (training (R2 correlation = 0.99 and RMSE error = 0.147) and testing (R2 correlation = 0.99 and RMSE error = 0.148)).
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16
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Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106510] [Citation(s) in RCA: 127] [Impact Index Per Article: 42.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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17
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Zhang Y, Liu R, Heidari AA, Wang X, Chen Y, Wang M, Chen H. Towards augmented kernel extreme learning models for bankruptcy prediction: Algorithmic behavior and comprehensive analysis. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.10.038] [Citation(s) in RCA: 130] [Impact Index Per Article: 43.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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18
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An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework. ENERGIES 2021. [DOI: 10.3390/en14041196] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for the early prediction of SIr in practice. The findings of this research may shed light on the use of advanced intelligent models for efficient energy development.
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Electrical Power Prediction through a Combination of Multilayer Perceptron with Water Cycle Ant Lion and Satin Bowerbird Searching Optimizers. SUSTAINABILITY 2021. [DOI: 10.3390/su13042336] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Predicting the electrical power (PE) output is a significant step toward the sustainable development of combined cycle power plants. Due to the effect of several parameters on the simulation of PE, utilizing a robust method is of high importance. Hence, in this study, a potent metaheuristic strategy, namely, the water cycle algorithm (WCA), is employed to solve this issue. First, a nonlinear neural network framework is formed to link the PE with influential parameters. Then, the network is optimized by the WCA algorithm. A publicly available dataset is used to feed the hybrid model. Since the WCA is a population-based technique, its sensitivity to the population size is assessed by a trial-and-error effort to attain the most suitable configuration. The results in the training phase showed that the proposed WCA can find an optimal solution for capturing the relationship between the PE and influential factors with less than 1% error. Likewise, examining the test results revealed that this model can forecast the PE with high accuracy. Moreover, a comparison with two powerful benchmark techniques, namely, ant lion optimization and a satin bowerbird optimizer, pointed to the WCA as a more accurate technique for the sustainable design of the intended system. Lastly, two potential predictive formulas, based on the most efficient WCAs, are extracted and presented.
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20
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Huang W, Cheng Q, Ma D. Recent reports on magnetic nanoparticles supported metallic catalysts: Synthesis of heterocycles. SYNTHETIC COMMUN 2021. [DOI: 10.1080/00397911.2021.1884882] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Wenquan Huang
- College of Mechanical and Automotive Engineering, Anhui Wenda University of Information Engineering, Hefei, P. R. China
| | - Qing Cheng
- Department of Computer and Information Engineering, Huainan Normal University, Huainan, P. R. China
| | - Dongsheng Ma
- College of Mechanical and Automotive Engineering, Anhui Wenda University of Information Engineering, Hefei, P. R. China
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Hu J, Chen H, Heidari AA, Wang M, Zhang X, Chen Y, Pan Z. Orthogonal learning covariance matrix for defects of grey wolf optimizer: Insights, balance, diversity, and feature selection. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106684] [Citation(s) in RCA: 123] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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22
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Fan Y, Wang P, Mafarja M, Wang M, Zhao X, Chen H. A bioinformatic variant fruit fly optimizer for tackling optimization problems. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2020.106704] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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23
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Sun G, Li C, Deng L. An adaptive regeneration framework based on search space adjustment for differential evolution. Neural Comput Appl 2021. [DOI: 10.1007/s00521-021-05708-1] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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24
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Multi-population following behavior-driven fruit fly optimization: A Markov chain convergence proof and comprehensive analysis. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.106437] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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25
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Abstract
Electrical conductivity (EC), one of the most widely used indices for water quality assessment, has been applied to predict the salinity of the Babol-Rood River, the greatest source of irrigation water in northern Iran. This study uses two individual—M5 Prime (M5P) and random forest (RF)—and eight novel hybrid algorithms—bagging-M5P, bagging-RF, random subspace (RS)-M5P, RS-RF, random committee (RC)-M5P, RC-RF, additive regression (AR)-M5P, and AR-RF—to predict EC. Thirty-six years of observations collected by the Mazandaran Regional Water Authority were randomly divided into two sets: 70% from the period 1980 to 2008 was used as model-training data and 30% from 2009 to 2016 was used as testing data to validate the models. Several water quality variables—pH, HCO3−, Cl−, SO42−, Na+, Mg2+, Ca2+, river discharge (Q), and total dissolved solids (TDS)—were modeling inputs. Using EC and the correlation coefficients (CC) of the water quality variables, a set of nine input combinations were established. TDS, the most effective input variable, had the highest EC-CC (r = 0.91), and it was also determined to be the most important input variable among the input combinations. All models were trained and each model’s prediction power was evaluated with the testing data. Several quantitative criteria and visual comparisons were used to evaluate modeling capabilities. Results indicate that, in most cases, hybrid algorithms enhance individual algorithms’ predictive powers. The AR algorithm enhanced both M5P and RF predictions better than bagging, RS, and RC. M5P performed better than RF. Further, AR-M5P outperformed all other algorithms (R2 = 0.995, RMSE = 8.90 μs/cm, MAE = 6.20 μs/cm, NSE = 0.994 and PBIAS = −0.042). The hybridization of machine learning methods has significantly improved model performance to capture maximum salinity values, which is essential in water resource management.
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26
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Predicting Coronary Atherosclerotic Heart Disease: An Extreme Learning Machine with Improved Salp Swarm Algorithm. Symmetry (Basel) 2020. [DOI: 10.3390/sym12101651] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
To provide an available diagnostic model for diagnosing coronary atherosclerotic heart disease to provide an auxiliary function for doctors, we proposed a new evolutionary classification model in this paper. The core of the prediction model is a kernel extreme learning machine (KELM) optimized by an improved salp swarm algorithm (SSA). To get a better subset of parameters and features, the space transformation mechanism is introduced in the optimization core to improve SSA for obtaining an optimal KELM model. The KELM model for the diagnosis of coronary atherosclerotic heart disease (STSSA-KELM) is developed based on the optimal parameters and a subset of features. In the experiment, STSSA-KELM is compared with some widely adopted machine learning methods (MLM) in coronary atherosclerotic heart disease prediction. The experimental results show that STSSA-KELM can realize excellent classification performance and more robust stability under four indications. We also compare the convergence of STSSA-KELM with other MLM; the STSSA-KELM model has demonstrated a higher classification performance. Therefore, the STSSA-KELM model can effectively help doctors to diagnose coronary heart disease.
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28
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Deng LB, Zhang LL, Fu N, Sun HL, Qiao LY. ERG-DE: An elites regeneration framework for differential evolution. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.05.108] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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29
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Chen L, Gao R, Bian Y, Di H. Elliptic entropy of uncertain random variables with application to portfolio selection. Soft comput 2020. [DOI: 10.1007/s00500-020-05266-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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30
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Zheng M, Zhang L, Feng Y, He L, Sun G. Credibility-based fuzziness and incomplete information value in fuzzy programming. EVOLUTIONARY INTELLIGENCE 2020. [DOI: 10.1007/s12065-020-00467-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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31
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Wang G, Ma M, Wang J, Xue H, Wang J. An option contract on emergency material reserve considering quality catastrophic change. EVOLUTIONARY INTELLIGENCE 2020. [DOI: 10.1007/s12065-020-00371-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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32
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WANG L, DONG C. Analysis of the model of integration of multicultural media in the network’s space of public opinion. TRANSINFORMACAO 2020. [DOI: 10.1590/2318-0889202032e200044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Abstract When the traditional Meta database model is used to integrate multicultural media in the network’s space of public opinion, the performance of cooperative processing of distributed heterogeneous network data is poor, the logic of integration of multicultural media information is poor, the integration time is long, and the accuracy is low. This article proposes a multicultural media integration model based on multi-agent collaboration, which combines the life cycle of multicultural media models in the network’s space of public opinion. In distributed heterogeneous network spaces of public opinion, data is obtained from the distributed multicultural media information source, transferred to the multi-agent, and the collaboration among the agents is constructed. The multicultural media integration model provides the work flow and the standard principle of the model, and shapes the integrated model’s architecture including the data layer, the middle layer, and the expression layer, so as to achieve the intelligent integration of the multicultural media information. The experimental results show that the proposed model has high practicability, high integration efficiency and accuracy, and can enhance the penetration of multicultural media brands.
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33
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Xu B, Zhang Z. Constrained Optimization Based on Ensemble Differential Evolution and Two-Level-Based Epsilon Method. IEEE ACCESS 2020; 8:213981-213997. [DOI: 10.1109/access.2020.3040647] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Affiliation(s)
- Bin Xu
- School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, China
| | - Zonghao Zhang
- School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, China
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34
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ZHANG D, SUN D. A model for art communication and development under the influence of social network. TRANSINFORMACAO 2020. [DOI: 10.1590/2318-0889202032e200047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Abstract Improving the value of art information and user behavior factors can boost the effect of art communication and development. This paper proposes a social network based on the s-seir (Single SEIR) art communication and development model, a new model developed based on the SEIR (Susceptible, Exposed, Infectious, Recovered) classical epidemic dynamics model. In addition, we present the concept and characteristics of art communication, summarize the rules of node classification and art information evolution, and design an interpretative s-seir model considering the value of art information and user behavior factors. The experimental results show that the model can clearly analyze the impact of art value and user behavior on the dissemination and development of art information, and has the advantages of high efficiency and accuracy.
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35
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Differential evolutionary algorithm with an evolutionary state estimation method and a two-level selection mechanism. Soft comput 2019. [DOI: 10.1007/s00500-019-04621-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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36
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