This article proposes a low-carbon operation analysis method for micro grids based on improved particle swarm optimization algorithm. Corresponding improvements have been made to the inertia weight, learning factor, a...
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This article proposes a low-carbon operation analysis method for micro grids based on improved particle swarm optimization algorithm. Corresponding improvements have been made to the inertia weight, learning factor, and individual extreme of the algorithm, depicting the comprehensive low-carbon operation information of micro grids under the influence of carbon emission quotas and carbon trading mechanisms from the perspective of data visualization. The low-carbon scheduling of micro grids is carried out from three perspectives: environmental protection, economy, and comprehensiveness, which compensates for the limitations of focusing on traditional low-carbon operation and provides a powerful tool for analyzing low-carbon operation of micro grids. Firstly, establish the energy consumption cost and carbon emission cost functions of the micro grid system, add the two cost functions together and take the minimum sum to form the objective function of this article. Then, based on the characteristics of each unit, a low-carbon model is constructed to constrain the carbon emissions of each unit. Finally, simulation analysis was conducted on the micro grid system based on the improved particle swarm optimization algorithm, verifying the effectiveness and practicality of the proposed algorithm. The simulation results show that the improved particle swarm optimization algorithm can quickly and effectively reduce energy consumption and carbon emission costs, and improve the comprehensive efficiency of micro grid systems.
Traditional sonar image target detection analysis has problems such as long detection time, low detection accuracy and slow detection speed. To solve these problems, this paper will use the multi-feature fusion sonar ...
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Traditional sonar image target detection analysis has problems such as long detection time, low detection accuracy and slow detection speed. To solve these problems, this paper will use the multi-feature fusion sonar image target detection algorithm based on the particle swarm optimization algorithm to analyze the sonar image. This algorithm uses the particleswarmalgorithm to optimize the combination of multiple feature vectors and realizes the adaptive selection and combination of features, thus improving the accuracy and efficiency of sonar image target detection. The results show that: when other conditions are the same, under the particle group optimizationalgorithm, the sonar image multiple feature detection algorithm for three sonar image detection time between 4s-9.9s, and the sonar image single feature detection algorithm of three sonar image detection time between 12s-20.9s, shows that the PSO in multiple feature fusion sonar image target detection with better performance and practicability, can be effectively applied to the sonar image target detection field.
Feature selection (FS) is a crucial preprocessing step that aims to eliminate irrelevant and redundant features, reduce the dimensionality of the feature space, and enhance clustering efficiency and effectiveness. FS ...
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Feature selection (FS) is a crucial preprocessing step that aims to eliminate irrelevant and redundant features, reduce the dimensionality of the feature space, and enhance clustering efficiency and effectiveness. FS is categorized as NP-Hard due to the high number of existing solutions. Various metaheuristic methods have been developed to address the FS problem, yielding promising results. Particularly, particleswarmoptimization (PSO), an evolutionary computing (EC) approach guided by swarm intelligence, has gained widespread adoption owing to its implementation simplicity and potential for global search. This paper analyzes several variants of PSO algorithms and introduces a new FS method called HPSO. The proposed approach utilizes an asynchronously adaptive inertia weight and an improved constriction factor. Additionally, it incorporates a chaotic map and a MAD fitness function with a feature count penalty to tackle the clustering FS problem. The efficiency of the developed method is evaluated against the genetic algorithm (GA) and well-known variants of PSO algorithms, including PSOs with fixed inertia weights, PSOs with improved inertia weights, PSOs with fixed constriction factors, PSOs with improved constriction factors, PSOs with adaptive inertia weights, and PSO's includes advanced learning exemplars and sophisticated structure topologies. This paper assesses two different reference text data sets, Reuters-21578 and Webkb. In comparison with competitive methods, the proposed HPSO method achieves higher clustering precision and selects a more informative feature set.
Frost heave poses a serious hazard to geotechnical engineering. However, conventional experimental and theoretical methods, which have limitations in accurately describing the deformation behavior of soils during fros...
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Frost heave poses a serious hazard to geotechnical engineering. However, conventional experimental and theoretical methods, which have limitations in accurately describing the deformation behavior of soils during frost heave, struggle due to the nonlinear and uncertain nature of the process. For this reason, the study leverages the advantages of the Generalized Regression Neural Network (GRNN) in handling nonlinear problems and small sample datasets. The structure of the GRNN model is further optimized using the particle swarm optimization algorithm (PSO) and K-fold Cross Validation (K). The input variables for the model include water content (W), W ), temperature (T), T ), dry density (rho), and plasticity index (Ip) I p ) under various working conditions. The frost heave rate (7) is considered as the output variable. Meanwhile, the model also considers the effects of both one-factor and two-factor interactions among the input variables on frost heave behaviors. Finally, a prediction model for 7 based on the K-PSO-GRNN is established. The results demonstrate that the K-PSO-GRNN model exhibits greater robustness and stability in predicting 7 compared to PSO-GRNN and GRNN (R2 2 = 0.94, MAE = 0.14), and the prediction residuals for 7 range from 0 to 0.4. Among these variables, W has the most significant influence on 7, followed by T , rho, and I p . Moreover, both rho and Ip p have significant interactions with T and have a notable impact on the soil's frost heave behavior. At high rho, the soil shows reduced sensitivity to frost heave in response to changes in T , while at high I p , the soil becomes more sensitive to frost heave with changes in T . 7 generally shows a positive correlation with W and rho, and a negative correlation with T . The aforementioned K-PSO-GRNN model can be utilized for predicting 7, which is valuable in forecasting non-uniform deformation hazards caused by frost heave and studying preventive measures.
Effectively combining various evolutionary computing algorithms and leveraging the advantages of each can significantly enhance the convergence speed and solution quality of the algorithm. However, a mere combination ...
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Effectively combining various evolutionary computing algorithms and leveraging the advantages of each can significantly enhance the convergence speed and solution quality of the algorithm. However, a mere combination of evolutionary computing algorithms may not comprehensively improve optimization performance and may even lead to poorer performance in certain optimization problems. The aim of the paper is to provide a fundamental integrating platform and method based on species explode and deracinate algorithm. Utilizing the species explode and deracinate algorithm as a foundation, this study presents a hybrid algorithm named SED-PSO algorithm by utilizing the particle swarm optimization algorithm as an exemplar. The outcomes of the simulations conducted on 27 benchmark functions published by the Competition on Evolutionary Constrained demonstrate that the SED-PSO algorithm exhibits exceptional convergence accuracy, robust stability, and rapid convergence speed. The simulation results comprehensively illustrate that the species explode and deracinate algorithm serves as a fundamental integrating platform for diverse evolutionary computing algorithms, while also incorporating the strengths of each algorithm. Additionally, the outcomes of the optimization of sensor network coverage reveal that the SED-PSO algorithm exhibits superior solution quality, minimal occurrence of local extremum, and enhanced stability and efficacy.
Maritime transportation has significantly contributed to global economic development but is also a major source of air pollution. This study aims to provide an inverse calculation framework of vessel emission source i...
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Maritime transportation has significantly contributed to global economic development but is also a major source of air pollution. This study aims to provide an inverse calculation framework of vessel emission source intensity for emission monitoring. This research enhanced the traditional Gaussian diffusion model by specific characteristics of ship emissions and various influencing factors identified through simulation experiments. An inverse model is developed using pattern search and particleswarmoptimization (PSO) algorithms to estimate marine exhaust source strengths. Results indicate that the PSO algorithm is the most accurate and efficient, especially with an iteration step size of 0.1 s. Practical application using data from 86 monitored ships revealed that 76 had fuel sulfur content exceeding the 0.1 % threshold, achieving an accuracy rate of 88.37 %. These findings are crucial for improving the understanding of marine exhaust dispersion and advancing remote monitoring technologies, contributing to better environmental management of maritime transport emissions.
Over the past decades, the software industry has expanded to include all industries. Since stakeholders tend to use it to get their work done, software houses seek to estimate the cost of the software, which includes ...
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Over the past decades, the software industry has expanded to include all industries. Since stakeholders tend to use it to get their work done, software houses seek to estimate the cost of the software, which includes calculating the effort, time, and resources required. Although many researchers have worked to estimate it, the prediction accuracy results are still inaccurate and unstable. Estimating it requires a lot of effort. Therefore, there is an urgent need for modern techniques that contribute to cost estimation. This paper seeks to present a model based on deep learning and machine learning techniques by combining convolutional neural networks (CNN) and the particleswarmalgorithm (PSO) in the context of time series forecasting, which enables feature extraction and automatic tuning of hyperparameters, which reduces the manual effort of selecting parameters and contributes to fine-tuning. The use of PSO also enhances the robustness and generalization ability of the CNN model and its iterative nature allows for efficient discovery of hyperparameter similarity. The model was trained and tested on 13 different benchmark datasets and evaluated through six metrics: mean absolute error (MAE), mean square error (MSE), mean magnitude relative error (MMRE), root mean square error (RMSE), median magnitude relative error (MdMRE), and prediction accuracy (PRED). Comparative results reveal that the performance of the proposed model is better than other methods for all datasets and evaluation criteria. The results were very promising for predicting software cost estimation.
Lithium-ion battery State of Health(SOH)estimation is an essential issue in battery management *** order to better estimate battery SOH,Extreme Learning Machine(ELM)is used to establish a model to estimate lithium-ion...
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Lithium-ion battery State of Health(SOH)estimation is an essential issue in battery management *** order to better estimate battery SOH,Extreme Learning Machine(ELM)is used to establish a model to estimate lithium-ion battery *** swarmoptimizationalgorithm(PSO)is used to automatically adjust and optimize the parameters of ELM to improve estimation ***,collect cyclic aging data of the battery and extract five characteristic quantities related to battery capacity from the battery charging curve and increment capacity *** Grey Relation Analysis(GRA)method to analyze the correlation between battery capacity and five characteristic ***,an ELM is used to build the capacity estimation model of the lithium-ion battery based on five characteristics,and a PSO is introduced to optimize the parameters of the capacity estimation *** proposed method is validated by the degradation experiment of the lithium-ion battery under different *** results show that the battery capacity estimation model based on ELM and PSO has better accuracy and stability in capacity estimation,and the average absolute percentage error is less than 1%.
particle swarm optimization algorithm is a widely used swarm intelligence algorithm. Aiming at the path length, algorithm stability, running time and other problems obtained by the PSO algorithm for solving the three-...
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particle swarm optimization algorithm is a widely used swarm intelligence algorithm. Aiming at the path length, algorithm stability, running time and other problems obtained by the PSO algorithm for solving the three-dimensional path planning problem, an improved particle swarm optimization algorithm based on Logistic function and trigonometric function is proposed to solve the three-dimensional path planning problem. Two types and six modification strategies for the inertia weights and learning factors of PSO algorithm are proposed, the two types are PSO algorithm based on the change of inertia weights omega of different functions, and LWDPSO algorithm and TCPSO algorithm based on the change of C-1 and C-2 of different learning functions, and these two algorithms are combined into an Improved particle swarm optimization algorithm (IPSO). Finally, the feasibility and effectiveness of the proposed method is verified by simulating the UAV flying over the mountain peaks, and the effect of the change of inertia weight and learning factor on the PSO algorithm is discussed by classifying the IPSO algorithm with the LWDPSO algorithm and the TCPSO algorithm, and finally comparing the IPSO algorithm with the PSO algorithm, the genetic algorithm, and the artificial swarmalgorithm, and the simulation results show that the proposed IPSO algorithm is more advantageous compared with the other algorithms. The simulation results show that the proposed IPSO algorithm is more advantageous than other algorithms and can better accomplish the path planning task.
Accurate flood forecasting in advance is crucial for planning and implementing watershed flood prevention measures. This study developed the PSO-TCN-Bootstrap flood forecasting model for the Tailan River Basin in Xinj...
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Accurate flood forecasting in advance is crucial for planning and implementing watershed flood prevention measures. This study developed the PSO-TCN-Bootstrap flood forecasting model for the Tailan River Basin in Xinjiang by integrating the particleswarm optimisation (PSO) algorithm, temporal convolutional network (TCN), and Bootstrap probability sampling method. Evaluated on 50 historical flood events from 1960 to 2014 using observed rainfall-runoff data, the model showed, under the same lead time conditions, a higher Nash efficiency coefficient, along with lower root mean square and relative peak errors in flood forecasting. These results highlight the PSO-TCN-Bootstrap model's superior applicability and robustness for the Tailan River Basin. However, when the lead time exceeds 5 h, the model's relative peak error remains above 20%. Future work will focus on integrating flood generation mechanisms and enhancing machine learning models' generalisability in flood forecasting. These findings provide a scientific foundation for flood management strategies in the Tailan River Basin.
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