Accurate aging state evaluation of traction transformer insulation at the hotspot region is crucial for ensuring the stable operation of the railway traffic system. Given this issue, this research begins by collecting...
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Accurate aging state evaluation of traction transformer insulation at the hotspot region is crucial for ensuring the stable operation of the railway traffic system. Given this issue, this research begins by collecting the polarization currents of oil-paper insulation. Then, the modified dielectric response model is proposed to simulate the collected polarization currents. To extract the dielectric feature parameters related to the insulating paper's aging state, the cooperative co-evolutionary algorithm (CCEA) modified by niche mechanisms (NMs) is proposed. During the iteration process, the inversion computation technique is devised to reduce the number of feature parameters to be optimized. Futhermore, the dielectric feature parameters database is established by the surface fitting technique, Finally, the database is employed to evaluate the aging state of hotspot insulating paper under both laboratory conditions and field transformers and correct the influence of testing temperature. The relative errors between the calculated and measured DP in verification experiments are under 4%, and the corresponding aging state labels are exactly the same. In this context, the study is anticipated to provide a precise assessment of the aging degree of traction transformer insulation at the hotspot region.
Accurate small-sample prediction is an urgent, very difficult, and challenging task due to the quality of data storage restricted in most realistic situations, especially in developing countries. The grey model perfor...
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Accurate small-sample prediction is an urgent, very difficult, and challenging task due to the quality of data storage restricted in most realistic situations, especially in developing countries. The grey model performs well in small-sample prediction. Therefore, a novel multivariate grey model is proposed in this study, called FBNGM (1, N, r), with a fractional order operator, which can increase the impact of new information and background value coefficient to achieve high prediction accuracy. The utilization of an intelligence optimization algorithm to tune the parameters of the multivariate grey model is an improvement over the conventional method, as it leads to superior accuracy. This study conducts two sets of numerical experiments on CO2 emissions to evaluate the effectiveness of the proposed FBNGM (1, N, r) model. The FBNGM (1, N, r) model has been shown through experiments to effectively leverage all available data and avoid the problem of overfitting. Moreover, it can not only obtain higher prediction accuracy than comparison models but also further confirm the indispensable importance of various influencing factors in CO2 emissions prediction. Additionally, the proposed FBNGM (1, N, r) model is employed to forecast CO2 emissions in the future, which can be taken as a reference for relevant departments to formulate policies.
Accurate estimation of reference crop evapotranspiration (ETo) is crucial for agricultural water management. As the simplified alternatives of the Penman-Monteith equation, empirical methods have been widely recommend...
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Accurate estimation of reference crop evapotranspiration (ETo) is crucial for agricultural water management. As the simplified alternatives of the Penman-Monteith equation, empirical methods have been widely recommended worldwide. However, its application is still limited to parameters localization varied with geographical and climatic conditions, therefore developing an excellent optimizationalgorithm for calibrating parameters is very necessary. Regarding the above requirement, the present study developed a novel improved Grey Wolf algorithm (MDSL-GWA) to optimize the most recommended ones among three types of ETo methods. After the optimization performance comparison among Least Square Method (LSM), Genetic algorithm (GA), Grey Wolf algorithm (GWA), and MDSL-GWA in four climatic regions of China, this study found that the Priestley-Taylor (PT) method was the best radiation-based (Rn-based) method and achieved better performance in temperate continental region (TCR), mountain plateau region (MPR), and temperate monsoon region (TMR) than other types. While the temperature-based (T-based) Hargreaves-Samani (HS) method performed best in subtropical monsoon region (SMR), further attaching better performance among the same type in TMR and TCR, while the Oudin method was the best T-based method in MPR. Moreover, the Romanenko method was better humidity-based (RH-based) in TCR and MPR, whereas the Brockamp-Wenner method exhibited higher in SMR and TMR. Furthermore, despite intelligence optimization algorithms significantly enhancing original ETo methods, the MDSL-GWA achieved best performance and outperformed other algorithms by 4.5-29.6% in determination coefficient (R2), 4.7-27.3% in nash-sutcliffe efficient (NSE), 3.7-44.4% in relative root mean square error (RRMSE), and 3.1-56.2% in mean absolute error (MAE), respectively. After optimization, the MDSL-GWA-PT was the most recommended ETo method in TMR, TCR, and MPR, and the median values of R2, NSE, RRMSE, and MAE ra
The grey wolf optimizer(GWO)is a swarm-based intelligence optimization algorithm by simulating the steps of searching,encircling,and attacking prey in the process of wolf *** with its advantages of simple principle an...
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The grey wolf optimizer(GWO)is a swarm-based intelligence optimization algorithm by simulating the steps of searching,encircling,and attacking prey in the process of wolf *** with its advantages of simple principle and few parameters setting,GWO bears drawbacks such as low solution accuracy and slow convergence speed.A few recent advanced GWOs are proposed to try to overcome these ***,they are either difficult to apply to large-scale problems due to high time complexity or easily lead to early *** solve the abovementioned issues,a high-accuracy variable grey wolf optimizer(VGWO)with low time complexity is proposed in this *** first uses the symmetrical wolf strategy to generate an initial population of individuals to lay the foundation for the global seek of the algorithm,and then inspired by the simulated annealing algorithm and the differential evolution algorithm,a mutation operation for generating a new mutant individual is performed on three wolves which are randomly selected in the current wolf individuals while after each iteration.A vectorized Manhattan distance calculation method is specifically designed to evaluate the probability of selecting the mutant individual based on its status in the current wolf population for the purpose of dynamically balancing global search and fast convergence capability of VGWO.A series of experiments are conducted on 19 benchmark functions from CEC2014 and CEC2020 and three real-world engineering *** 19 benchmark functions,VGWO’s optimization results place first in 80%of comparisons to the state-of-art GWOs and the CEC2020 competition winner.A further evaluation based on the Friedman test,VGWO also outperforms all other algorithms statistically in terms of robustness with a better average ranking value.
This paper considers an optimal control problem of switched dynamical systems with control input and system state constraints. Unlike in traditional switched dynamical systems, the switching times cannot be specified ...
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This paper considers an optimal control problem of switched dynamical systems with control input and system state constraints. Unlike in traditional switched dynamical systems, the switching times cannot be specified directly and they are governed by a state-dependent switching condition. Thus, the existing methods cannot be directly used to solve this problem. To overcome this difficulty, the switching conditions are transformed into a continuous-time inequality constraint by introducing an integer constraint. Further, the original optimal control problem is approximated by using a sequence of constrained non-convex nonlinear parameter optimization problems by using a relaxation method, a control vector parameterization technique, and a time-scaling transformation. Following that, a penalty function-based intelligent optimizationalgorithm is proposed for obtaining a global optimal solution based on a more effective penalty function method and a more effective intelligent optimizationalgorithm. The convergence results show that the proposed method is globally convergent. Numerical simulation results show that the proposed method is lower time-consuming, has faster convergence speed, can obtain a better objective function value than the existing typical algorithms, and can achieve a stable and robust performance when considering the small perturbations in constraint conditions or the small perturbations of the model parameters. (C) 2021 Elsevier B.V. All rights reserved.
The development of integrated energy systems (IESs) faces two key challenges: ensuring the availability of renewable energy and enhancing the economic benefits of renewable energy utilization. The integrated energy sy...
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Accurate estimates of reference evapotranspiration (ET0) are of great significance to water resources planning and management, but the actual solar radiation (Rs), as the primary parameter for ET0 estimation, is diffi...
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Accurate estimates of reference evapotranspiration (ET0) are of great significance to water resources planning and management, but the actual solar radiation (Rs), as the primary parameter for ET0 estimation, is difficult to obtain directly in most areas. Thus, studying the impacts of locally calibrated empirical solar radiation (Rs) models to improve the accuracy of Penman-Monteith (PM) is significant. Meanwhile, swarm intelligencealgorithms have proved their potential in the domains of agriculture and hydrology, but few studies applied them in optimizing Rs models to improve ET0 estimation. This study used the particle swarm optimization (PSO), the gravitational search algorithm (GSA), and the mind evolutionary algorithm (MEA), respectively, to optimize the nine most common empirical Rs models, comprising three sunshine-based models (Angstrom, & BULL;Ogelman, Bahel), three temperature-based models (Hargreaves, Bristow-Campbell, Hunt), and three combined-based models (Fan, Chen, El-Sebaii), and then integrated them into the PM for ET0 estimation at four climatic zones of China. The results showed that the Fan model obtained the most accurate Rs estimates in china, while the sunshine-based and temperature-based exerted significantly different applicability at different climatic zones. Regarding optimizationalgorithm, this study found that GSA performed better for the & BULL;Ogelman model, Fan model, and Chen model when integrating into the PM equation for ET0 estimation, whereas MEA performed better for the Angstrom model, Hunt model, and El-Sebaii model. After optimization, PMGSA-Fan obtained the most accurate estimates of ET0 at four climatic zones, with a regional spatial gradient in estimates accuracy of Rs from north to south of China. In terms of sunshine-based models, the PMMEA-Angstrom performed better in TMZ, TCZ, and MPZ, whereas the PMGSA-O & BULL;gelman performed better in SMZ, respectively;in terms of temperature-based models, the PMMEA-Hunt performed
The paper analyses the significance of reactive power optimization,generalizes the current situation of power system *** optimizationalgorithms were introduced in this paper such as traditional optimizationalgorithm...
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The paper analyses the significance of reactive power optimization,generalizes the current situation of power system *** optimizationalgorithms were introduced in this paper such as traditional optimizationalgorithm,intelligence optimization algorithm,including the methods of linear programming,Newton's method,heuristic optimizationalgorithm,*** research analyzes the advantages and disadvantages of each algorithm and its application direction by comparing their outstanding performance in solving discrete variables and continuous *** purpose of the research is to find the optimal solution of reactive power optimizationalgorithm,minimize the transport network loss of power system,and improve the quality of users.
Grey Wolf Optimizer (GWO) and Particle Swarm optimization (PSO) algorithm are two popular swarm intelligence optimization algorithms and these two algorithms have their own search mechanisms. Based on their unique sea...
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Grey Wolf Optimizer (GWO) and Particle Swarm optimization (PSO) algorithm are two popular swarm intelligence optimization algorithms and these two algorithms have their own search mechanisms. Based on their unique search mechanisms and their advantages after the improvements on them, this paper proposes a novel hybrid algorithm based on PSO and GWO (Hybrid GWO with PSO, HGWOP). Firstly, GWO is simplified and a novel differential perturbation strategy is embedded in the search process of the simplified GWO to form a Simplified GWO with Differential Perturbation (SDPGWO) so that it can improve the global search ability while retaining the strong exploitation ability of GWO. Secondly, a stochastic mean example learning strategy is applied to PSO to create a Mean Example Learning PSO (MELPSO) to enhance the global search ability of PSO and prevent the algorithm from falling into local optima. Finally, a poor-for-change strategy is proposed to organically integrate SDPGWO and MELPSO to obtain an efficient hybrid algorithm of GWO and PSO. HGWOP can give full play to the advantages of these two improved algorithms, overcome the shortcomings of GWO and PSO and maximize the whole performance. A large number of experiments on the complex functions from CEC2013 and CEC2015 test sets reveal that HGWOP has better optimization performance and stronger universality compared with quite a few state-of-the-art algorithms. Experimental results on K-means clustering optimization show that HGWOP has obvious advantages over the comparison algorithms. (C) 2020 Elsevier B.V. All rights reserved.
This article focuses on how to design an efficient GPU-based chicken swarm optimization (CSO) algorithm (GCSO), so as to improve diversity and speed up convergence by running a large number of populations in parallel....
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This article focuses on how to design an efficient GPU-based chicken swarm optimization (CSO) algorithm (GCSO), so as to improve diversity and speed up convergence by running a large number of populations in parallel. GCSO mainly improves the sequential CSO in three aspects: (i) GCSO modifies the location updating equation of the rooster and proposes a parallel iterative strategy to transform the sequential iteration process into a parallel iterative process, thereby achieving fine-grained parallelism and improving the convergence speed. (ii) A multirange search strategy is proposed to build different neighborhoods for each flock on the graphic process units (GPU), so that each flock searched in their respective neighborhoods, thus increasing the density and diversity of the search, and making it not easy to fall into a local optimum. (iii) A new column storage structure is designed to meet the requirement of coalescent access on GPU. Twelve benchmark functions are selected to compare GCSO algorithm with some sequential intelligence optimization algorithms and the GPU-based particle swarm algorithm. The results show that the GCSO is able to obtain a speedup up to163.09xcompared with the CSO and achieve better optimization results in terms of both optimization accuracy and convergence speed than some intelligence optimization algorithms.
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