With the rapid development of artificial intelligence and data science, Dynamic Bayesian Network (DBN), as an effective probabilistic graphical model, has been widely used in many engineering fields. And swarm intelli...
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With the rapid development of artificial intelligence and data science, Dynamic Bayesian Network (DBN), as an effective probabilistic graphical model, has been widely used in many engineering fields. And swarmintelligencealgorithm is an optimizationalgorithm based on natural selection with the characteristics of distributed, self-organization and robustness. By applying the high-performance swarmintelligencealgorithm to DBN structure learning, we can fully utilize the algorithm's global search capability to effectively process time-based data, improve the efficiency of network generation and the accuracy of network structure. This study proposes an improved bacterial foraging optimizationalgorithm (IBFO-A) to solve the problems of random step size, limited group communication, and the inability to maintain a balance between global and local searching. The IBFO-A algorithm framework comprises four layers. First, population initialization is achieved using a logistics-sine chaotic mapping strategy as the basis for global optimization. Second, the activity strategy of a colony foraging trend is constructed by combining the exploration phase of the Osprey optimizationalgorithm. Subsequently, the strategy of bacterial colony propagation is improved using a "genetic" approach and the Multi-point crossover operator. Finally, the elimination-dispersal activity strategy is employed to escape the local optimal solution. To solve the problem of complex DBN learning structures due to the introduction of time information, a DBN structure learning method called IBFO-D, which is based on the IBFO-A algorithm framework, is proposed. IBFO-D determines the edge direction of the structure by combining the dynamic K2 scoring function, the designed V-structure orientation rule, and the trend activity strategy. Then, according to the improved reproductive activity strategy, the concept of "survival of the fittest" is applied to the network candidate solution while maintaining spec
With the rapid development of renewable energy, photovoltaic energy storage systems (PV-ESS) play an important role in improving energy efficiency, ensuring grid stability and promoting energy transition. As an import...
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With the rapid development of renewable energy, photovoltaic energy storage systems (PV-ESS) play an important role in improving energy efficiency, ensuring grid stability and promoting energy transition. As an important part of the micro-grid system, the energy storage system can realize the stable operation of the micro-grid system through the design optimization and scheduling optimization of the photovoltaic energy storage system. The structure and characteristics of photovoltaic energy storage system are summarized. From the perspective of photovoltaic energy storage system, the optimization objectives and constraints are discussed, and the current main optimizationalgorithms for energy storage systems are compared and evaluated. The challenges and future development of energy storage systems are briefly described, and the research results of energy storage system optimization methods are summarized. This paper summarizes the application of swarm intelligence optimization algorithm in photovoltaic energy storage systems, including algorithm principles, optimization goals, practical application cases, challenges and future development directions, providing new ideas for better promotion and application of new energy photovoltaic energy storage systems and valuable reference.
Accurately forecasting energy consumption is beneficial and pivotal for effectively managing variable refrigerant flow (VRF) systems. Changes in energy consumption provide an intuitive representation of the operating ...
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Accurately forecasting energy consumption is beneficial and pivotal for effectively managing variable refrigerant flow (VRF) systems. Changes in energy consumption provide an intuitive representation of the operating condition and the impact of possible faults. Energy consumption prediction is fundamental in energy conservation tasks such as fault diagnosis. Currently, many energy consumption prediction model have been applied for HVAC system widely. However black-box energy prediction models applied to VRF systems are still relatively few and crude. This study therefore proposed a hybrid energy consumption prediction method for VRF systems based on data partitioning and swarmintelligencealgorithm. The model was based on back propagation neural network (BPNN), and utilized data partitioning techniques to identify the energy consumption patterns of the VRF system. For each pattern, a BPNN sub-model was trained using operating and energy consumption data of a VRF system. Then, swarmintelligencealgorithm is adopted to determine the optimal architecture of each BPNN sub-model. The results demonstrate that the proposed hybrid models can achieve better prediction performance than single models like BP Neural Network. Root mean square error and mean absolute percentage error of the proposed predictive model is 202.49 and 1.95%, which has a 31.3% and 45.5% decrease compared to single BPNN model. At the same time, it is enlightening for other researchers to study the potential of data partitioning algorithm and swarmintelligencealgorithm in the field of VRF system energy consumption prediction.
This study, proposes the use of a novel rubber-sand concrete (RSC) material, which comprises rubber particles, sand, and cement, as an aseismic material in practical engineering construction. The uniaxial compressive ...
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This study, proposes the use of a novel rubber-sand concrete (RSC) material, which comprises rubber particles, sand, and cement, as an aseismic material in practical engineering construction. The uniaxial compressive strength (UCS) of damping materials is an important factor that directly affects the seismic activity in underground structures. To predict the UCS of RSC, artificial intelligence model back propagation neural network (BPNN), which is optimized through four swarmintelligenceoptimization (SIO) algorithms: particle swarmoptimizationalgorithm (PSO), fruit fly optimizationalgorithm (FOA), lion swarmoptimizationalgorithm (LSO), and sparrow search algorithm (SSA), is used. The dataset for the prediction models was obtained from uniaxial compression tests in the RSC laboratory. The performances of the four hybrid intelligence models were evaluated using six performance indicators: the root mean square error (RMSE), correlation coefficient (R), determination coefficient (R-2), mean absolute error (MAE), mean square error (MSE), and sum of square error (SSE).The prediction capability of these models was graded based on these indicators using a ranking system. The results show that the prediction ability of the LSO-BPNN hybrid model is better than that of the three other hybrid models, with RMSE of (1.0635, 1.2352), R of (0.9887, 0.9713), R-2 of (0.9776, 0.9165), MAE of (0.7257, 0.8243), MSE of (1.1352, 1.5256), SSE of (64.7074, 36.6151), and ranking score of (24, 24) in the training and testing phases, respectively. Therefore, the LSO-BPNN hybrid model is an efficient and accurate method for predicting the UCS of RSCs. Sensitivity analysis showed that rubber and sand were the most important elements that affected UCS prediction, followed by cement, with the lowest relative importance being RPZ. This study provides guidance for the extension and application of RSC materials to underground seismic engineering.
Aiming at the defects of the Aquila optimizer (AO) in dealing with some complex optimization problems, such as slow convergence speed, low convergence accuracy, and easy to fall into local optimum, in this paper, a hy...
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Aiming at the defects of the Aquila optimizer (AO) in dealing with some complex optimization problems, such as slow convergence speed, low convergence accuracy, and easy to fall into local optimum, in this paper, a hybrid Aquila optimizer (HAO) algorithm based on Gauss map and crisscross operator is proposed. First, Gauss map is introduced to initialize the Aquila population to improve the quality of the initial population. Then use the crisscross operator to promote the exchange of information within the population and maintain the diversity of the population in each iteration, which not only enhances the ability of the algorithm to jump out of the local optimum but also accelerates the global convergence of the algorithm. The results of experiments using 21 classical benchmark functions indicate that HAO has better global search ability, faster convergence speed, and better stability than AO. The overall optimization performance of HAO in different dimensions is better than particle swarmoptimization (PSO) algorithm, gray wolf optimization (GWO) algorithm, whale optimizationalgorithm (WOA), and crisscross optimization (CSO) algorithm. Finally, the results of K-means clustering optimization on six University of California (UCI) standard data sets demonstrate that HAO has significant advantages over three algorithms that are good at clustering optimization.
Traditional access point (AP) deployment is based on the principle of minimum area coverage, which takes the AP signal range as the radius and performs a geometric calculation to determine the deployment location. The...
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Traditional access point (AP) deployment is based on the principle of minimum area coverage, which takes the AP signal range as the radius and performs a geometric calculation to determine the deployment location. These methods lack support for various contexts and requirements, making them only useful in a conventional rectangular environment. As a result, we design a fine-grained AP deployment (FineAP) strategy, which first uses ray-tracing to determine the signal distribution of various locations in the environment, and then uses a hybrid swarm intelligence optimization algorithm based on the principle of signal matrix maximization to determine the AP deployment location. When compared to traditional deployment methods, FineAP can handle a variety of application scenarios with close to upbound performance and the least minimum of overhead.
A two-layer implicit difference scheme is employed in the present study to determine the temperature distribution in an asphalt *** calculation of each layer only needs four iterations to achieve ***,in order to impro...
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A two-layer implicit difference scheme is employed in the present study to determine the temperature distribution in an asphalt *** calculation of each layer only needs four iterations to achieve ***,in order to improve the calculation accuracy a swarm intelligence optimization algorithm is also exploited to inversely analyze the laws by which the thermal physical parameters of the asphalt pavement materials change with *** the basic cuckoo and the gray wolf algorithms,an adaptive hybrid optimizationalgorithm is obtained and used to determine the relationship between the thermal diffusivity of two types of asphalt pavement materials and the *** shown by the results,the prediction accuracy achievable with this approach is higher than that of the linear model.
Combination forecasting is an effective method for improving the accuracy of tourism demand. This study proposes an innovative combination strategy based on a multi-objective swarm intelligence optimization algorithm ...
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Combination forecasting is an effective method for improving the accuracy of tourism demand. This study proposes an innovative combination strategy based on a multi-objective swarm intelligence optimization algorithm and, for the first time, examines whether and how this algorithm can enhance the performance of tourism demand combination forecasting. An empirical study conducted under several scenarios demonstrates that the proposed combination strategy enhances the interaction among single forecasts, leading to improved forecast accuracy and stability compared with traditional combination methods. The model remained effective even during the COVID-19 pandemic. The findings have a positive impact on predictive research, offering new insights and methodologies for tourism demand modeling.
Photovoltaic (PV) systems are among the representatives of renewable energy technologies, and their performance is influenced by parameter configurations. This paper utilizes the swarm-elite learning mechanism's L...
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ISBN:
(数字)9789819755783
ISBN:
(纸本)9789819755776;9789819755783
Photovoltaic (PV) systems are among the representatives of renewable energy technologies, and their performance is influenced by parameter configurations. This paper utilizes the swarm-elite learning mechanism's Levy flight and Quadratic interpolation strategies to enhance the optimization performance of the Hippopotamus optimizationalgorithm (HO) in both the exploration and exploitation stages. The proposed algorithm is referred to as LQHO. The aim of this paper is to utilize LQHO to provide a high-quality solution for parameter extraction problems in various types of PV models. Ten representative CEC2017 functions and three PV models are selected for designing experiments to evaluate the optimization performance and parameter extraction capability of LQHO. Five advanced metaheuristic algorithms are chosen to design control group experiments. The results indicate that LQHO exhibits superior performance over its competitors in both the parameter extraction problems of the tenCEC2017 functions and the three PV models. This superiority is reflected in terms of solution accuracy, convergence speed, and robustness.
A recently developed swarmintelligence method that converges more quickly than some existing algorithms is called the dung beetle optimizationalgorithm (DBO). However, the algorithm still suffers from the disadvanta...
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ISBN:
(纸本)9798400716751
A recently developed swarmintelligence method that converges more quickly than some existing algorithms is called the dung beetle optimizationalgorithm (DBO). However, the algorithm still suffers from the disadvantage that the initial population is not uniform and easily falls into the global optimal solution. In this paper, a multistrategy fusion dung beetle optimizationalgorithm (MSFDBO) is proposed which is inspired by the sine search algorithm, whale optimizationalgorithm, and reverse learning algorithm. First, the initial population of dung beetles is initialized using the singer chaos function to obtain a more uniform initial population. In view of the rolling behavior of dung beetles, adaptive coefficients and sine search strategies are introduced to enhance the search range of the algorithm, and the amplitude of the sine function is changed through the adaptive coefficients to balance the local and global searches of the algorithm. In view of the foraging behavior, egg-laying behavior and stealing behavior of dung beetles, an adaptive spiral search strategy is introduced to improve the optimization ability of the algorithm in the solution space. For the optimal solution obtained in each iteration, a convex lens imaging reverse learning strategy is used to perturb the optimal solution to avoid the dung beetle population from falling into a local optimum during the iteration process. By comparing the benchmark test function and the Wilcoxon rank sum test, MSFDBO shows better convergence performance, optimization performance, and robust performance.
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