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Distribution Network Reconfiguration Using Chaotic Particle Swarm Chicken Swarm Fusion optimization algorithm

ENERGIES 2023年第20期16卷 7185页

作者： Wu, Yanmin Liu, Jiaqi Wang, Lu An, Yanjun Zhang, Xiaofeng Naval Univ Engn Coll Elect Engn Wuhan 430033 Peoples R China Zhengzhou Univ Light Ind Coll Bldg Environm Engn Zhengzhou 450002 Peoples R China

Aiming at the problems of traditional optimization algorithms for reconfiguring distribution networks, which easily fall into a local optimum, have difficulty finding a global optimum, and suffer from low computational efficiency, the proposed algorithm named Chaotic Particle Swarm Chicken Swarm Fusion optimization (CPSCSFO) is used to optimize the reconfiguration of the distribution network with distributed generation (DG). This article works to solve the problems mentioned above from the following three aspects: Firstly, chaotic formula is used to improve the initialization of the particles and optimize the optimal position. This increases individual randomness while avoiding local optimality for inert particles. Secondly, chicken swarm optimization (CSO) and particle swarm optimization (PSO) are combined. The multi-population nature of the CSO algorithm is used to increase the global search capability, and, at the same time, the information exchange between groups is completed to extend the particle search range, which ensures the independence and excellence of each particle group. Thirdly, the node hierarchy method is introduced to calculate the power flow. The branching loop matrix and the node hierarchy strategy are used to detect the network topology. In this way, improper solutions can be reduced, and the efficiency of the algorithm can be improved. This paper has demonstrated better performance by CPSCSFO based on simulation results. The network loss has been reduced and the voltage level of each node in the optimal reconfiguration with distributed power supply has been improved;the network loss in the optimal reconfiguration with DG is 69.59% lower than that reconfiguration before. The voltage level of each node is increased, the minimum node voltage is increased by 3.44% and a better convergence speed is presented. As a result, the quality of network operation and the distribution network is greatly improved and provides guidance for building a safer, mor

关键词： distribution network reconfiguration chaos optimization CPSCSFO algorithm optimization algorithm distributed generation

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Three-dimensional path planning for AUVs in ocean currents environment based on an improved compression factor particle swarm optimization algorithm

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OCEAN ENGINEERING 2023年第1期280卷

作者： Li, Xiaohong Yu, Shuanghe Dalian Maritime Univ Coll Marine Elect Engn Dalian 116026 Peoples R China Dalian Polytech Univ Sch Informat Sci & Engn Dalian 116034 Peoples R China

Three-dimensional path planning for autonomous underwater vehicles (AUVs) in underwater environments is the key to ensuring safe navigation and reliable mission completion. To obtain a safe and smooth three-dimensional path for an AUV in ocean currents and seabed obstacle environments, an improved compression factor particle swarm optimization algorithm is proposed. First, a three-dimensional seabed environment model and Lamb vortex current environment model are constructed. Second, by considering optimization objectives such as travel distance cost, seabed terrain constraints and ocean current constraints, a three-dimensional path planning mathematical model is constructed. Finally, an improved compression factor particle swarm optimization algorithm is proposed and applied to solve the multi-objective nonlinear optimization problem. To verify the optimization performance of the new algorithm, its optimization results are compared with those of other algorithms by minimizing the fitness value. The experimental results reveal that the improved compressed factor particle swarm optimal algorithm has better planning efficiency, path quality, and shorter planning time, which provides a new effective method for path planning of autonomous underwater vehicle.

关键词： Improved compression factor particle swarm optimization algorithm Autonomous underwater vehicle Three-dimensional Path planning Ocean currents

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A novel UAV path planning approach: Heuristic crossing search and rescue optimization algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 215卷

作者： Zhang, Chaoqun Zhou, Wenjuan Qin, Weidong Tang, Weidong Guangxi Minzu Univ Coll Artificial Intelligence Nanning 530006 Peoples R China Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

Unmanned aerial vehicle (UAV) path planning plays an important role in the flight process of an UAV, which needs an effective algorithm to deal with UAV path planning problem. The search and rescue optimization algorithm (SAR) is easy to implement and has the characteristics of flexible, but it has slow convergence speed and has not been applied to UAV path planning. To address these problems, a heuristic crossing search and rescue optimization algorithm (HC-SAR) is proposed. HC-SAR combines a heuristic crossover strategy with the basic SAR to improve the convergence speed and maintain the population diversity in the optimization process. Furthermore, a real-time path adjustment strategy is proposed to straighten the UAV flight path. In addition, cubic B-spline interpolation is used to smooth the generated path. Comprehensive experiments including two-dimensional and three-dimensional environments for different threat zone are conducted to validate the performance of HC-SAR. The results show that HC-SAR has a high convergence speed and can successfully obtain a safe and efficient path, and it significantly outperforms SAR, differential evolution (DE), ant lion optimizer (ALO), squirrel search algorithm (SSA) and salp swarm algorithm (SSA) in all the cases. These results suggest that the proposed algorithm can effectively serve as an alternative for solving UAV path planning problem.

关键词： Path planning Heuristic crossing search and rescue optimization algorithm Search and rescue optimization algorithm Path adjustment Unmanned aerial vehicle

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Prediction of Ultimate Bearing Capacity of Pile Foundation Based on Two optimization algorithm Models

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BUILDINGS 2023年第5期13卷 1242页

作者： Ren, Jiajun Sun, Xianbin Hubei Univ Technol Sch Civil Engn Architecture & Environm Wuhan 430068 Peoples R China

The determination of the bearing capacity of pile foundations is very important for their design. Due to the high uncertainty of various factors between the pile and the soil, many methods for predicting the ultimate bearing capacity of pile foundations focus on correlation with field tests. In recent years, artificial neural networks (ANN) have been successfully applied to various types of complex issues in geotechnical engineering, among which the back-propagation (BP) method is a relatively mature and widely used algorithm. However, it has inevitable shortcomings, resulting in large prediction errors and other issues. Based on this situation, this study was designed to accomplish two tasks: firstly, using the genetic algorithm (GA) and particle swarm optimization (PSO) to optimize the BP network. On this basis, the two optimization algorithms were improved to enhance the performance of the two optimization algorithms. Then, an adaptive genetic algorithm (AGA) and adaptive particle swarm optimization (APSO) were used to optimize a BP neural network to predict the ultimate bearing capacity of the pile foundation. Secondly, to test the performance of the two optimization models, the predicted results were compared and analyzed in relation to the traditional BP model and other network models of the same type in the literature based on the three most common statistical indicators. The models were evaluated using three common evaluation metrics, namely the coefficient of determination (R-2), value account for (VAF), and the root mean square error (RMSE), and the evaluation metrics for the test set were obtained as AGA-BP (0.9772, 97.8348, 0.0436) and APSO-BP (0.9854, 98.4732, 0.0332). The results show that compared with the predicted results of the BP model and other models, the test set of the AGA-BP model and APSO-BP model achieved higher accuracy, and the APSO-BP model achieved higher accuracy and reliability, which provides a new method for the prediction of the ul

关键词： pile foundation ultimate bearing capacity AGA-BP APSO-BP optimization algorithm

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Accuracy assessment and improvement of SRTM, ASTER, FABDEM, and MERIT DEMs by polynomial and optimization algorithm: A case study (Khuzestan Province, Iran)

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OPEN GEOSCIENCES 2023年第1期15卷

作者： Saberi, Azim Kabolizadeh, Mostafa Rangzan, Kazem Abrehdary, Majid Shahid Chamran Univ Ahvaz Fac Earth Sci Dept RS & GIS Ahvaz Iran Univ West Trollhattan Sweden

Satellite digital elevation models (DEMs) are used for decision-making in various fields. Therefore, evaluating and improving vertical accuracy of DEM can increase the quality of end products. This article aimed to increase the vertical accuracy of most popular satellite DEMs (i.e., the ASTER, Shuttle Radar Topography Mission [SRTM], Forest And Buildings removed Copernicus DEM [FABDEM], and Multi-Error-Removed Improved-Terrain [MERIT]) using the particle swarm optimization (PSO) algorithm. For this purpose, at first, the vertical error of DEMs was estimated via ground truth data. Next, a second-order polynomial was applied to model the vertical error in the study area. To select the polynomial with the highest accuracy, employed for vertical error modeling, the coefficients of the polynomial have been optimized using the PSO algorithm. Finally, the efficiency of the proposed algorithm has been evaluated by other ground truth data and in situ observations. The results show that the mean absolute error (MAE) of SRTM DEM is 4.83 m while this factor for ASTER DEM is 5.35 m, for FABDEM is 4.28, and for MERIT is 3.87. The obtained results indicated that the proposed model could improve the MAE of vertical accuracy of SRTM, ASTER, FABDEM, and MERIT DEMs to 0.83, 0.51, 0.37, and 0.29 m, respectively.

关键词： SRTM ASTER DEM DEM accuracy assessment optimization algorithm DEM accuracy improvement

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Performance optimization of different Raman amplifier configurations for improved gain using multiparameter optimization algorithm

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OPTICAL AND QUANTUM ELECTRONICS 2023年第4期55卷 385-385页

作者： Javaid, Zaid Bin Obaid, Hafiz Muhammad Ahmad, Ishtiaq Univ Lahore Dept Elect Engn Lahore Pakistan Punjab Tianjin Univ Technol Dept Elect Engn Technol Lahore Pakistan Natl Skills Univ Dept Elect Engn Technol Islamabad Pakistan

This paper proposed three different Raman optical amplifier architectures that are designed and investigated for 50 x 100 Gbps dense wavelength division multiplexed (DWDM) system at channel spacing of 0.8 nm. The performance is determined and compared in terms of gain, gain ripple, noise figure (NF), Q-factor and bit error rate (BER) in the C-band at wavelengths in the vicinity of 1550 nm. Pump powers of the Raman amplifier are selected using multiparameter optimization algorithm to achieve maximum gain with small ripple. The effects of varying input powers on gain, gain ripple and NF are also investigated. Raman amplifier with bidirectional pumping configuration achieves highest average gain of 21.42 dB with lowest gain ripple of 1.24 dB. Average gain of 21.37 dB with gain ripple of 1.55 dB is obtained for forward pumping configuration. For backward pumping configuration, an average gain of 21.32 dB is obtained. All three configurations achieved a good quality factor (> 12.0 dB) at each wavelength. The utility of these Raman optical amplifier architectures in long-distance DWDM systems is validated by the obtained high gain, improved gain flatness, good Q-factor, and low BER.

关键词： Raman optical amplifier DWDM Backward pumping Forward pumping Bidirectional pumping Flat gain optimization algorithm

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Study on a Hybrid Hydrological Forecasting Model SCE-GUH by Coupling SCE-UA optimization algorithm and General Unit Hydrograph

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WATER 2023年第15期15卷 2783-2783页

作者： Xu, Yingying Liu, Chengshuai Yu, Qiying Zhao, Chenchen Quan, Liyu Hu, Caihong Zhengzhou Univ Yellow River Lab Zhengzhou 450001 Peoples R China

Implementing real-time prediction and warning systems is an effective approach for mitigating flash flood disasters. However, there is still a challenge in improving the accuracy and reliability of flood prediction models. This study develops a hydrological prediction model named SCE-GUH, which combines the Shuffled Complex Evolution-University of Arizona optimization algorithm with the general unit hydrograph routing method. Our aims were to investigate the applicability of the general unit hydrograph in runoff calculations and its performance in predicting flash flood events. Furthermore, we examined the influence of parameter variations in the general unit hydrograph on flood simulations and conducted a comparative analysis with the conventional Nash unit hydrograph. The research findings demonstrate that the utilization of the general unit hydrograph method can considerably decrease computational errors and enhance prediction accuracy. The flood peak detection rate was found to be 100% in all four study watersheds. The average Nash-Sutcliffe efficiency coefficients were 0.83, 0.83, 0.84, and 0.87, while the corresponding coefficients of determination were 0.86, 0.85, 0.86, and 0.94, and the absolute errors of peak present time were 0.19 h, 0.40 h, 0.91 h, and 0.82 h, respectively. Moreover, the utilization of the general unit hydrograph method was found to significantly reduce the peak-to-current time difference, thereby enhancing simulation accuracy. Parameter variations have a substantial influence on peak flow characteristics. The SCE-GUH model, which incorporates the topographic and geomorphological features of the watershed along with the optimization algorithm, is capable of effectively characterizing the catchment properties of the watershed and offers valuable insights for enhancing the early warning and prediction of hydrological forecasting.

关键词： general unit hydrograph rainfall-runoff relationship optimization algorithm flash flood simulation application test surface confluence

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A reconfiguration method for photovoltaic array of stratospheric airship based on multilevel optimization algorithm

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APPLIED ENERGY 2023年 352卷

作者： Shan, Chuan Sun, Kangwen Ji, Xinzhe Cheng, Dongji Beihang Univ Sch Aeronaut Sci & Engn 37 Xueyuan Rd Beijing 100191 Peoples R China Beihang Univ Yunnan Innovat Inst 8 Shibo Rd Kunming 650233 Peoples R China

Reducing the mismatch loss to increase the output power of the photovoltaic (PV) array is crucial for extending the flight time of stratospheric airships. This paper presents a reconfiguration system for PV arrays based on a switch matrix designed for stratospheric airships. The proposed system employs a multilevel optimization reconfiguration algorithm that combines smart choice, greedy, and Munkres' assignment algorithms. Simulations were conducted under single working conditions, full-day sunlight cycles, and full-year PV array reconfigurations, respectively. The results demonstrated that the reconfigured PV array significantly improved the output power with a smooth P-V curve. The instantaneous power under extreme working conditions could be increased by 50.1%. Furthermore, during the 7-day simulation process, the average daily power output of the PV array increased by 14.68%, whereas the output fluctuation during circular cruising was reduced. The reconfiguration system offers greater advantages during months with weak irradiance in high-latitude regions, where the daily output power of the PV array can be increased by up to 24.46%. This significantly reduces the installation area and weight ratio of a stratospheric airship PV array.

关键词： Photovoltaic array Stratospheric airship Reconfiguration optimization algorithm Mismatch loss Energy improvement

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Feature Selection and SVM Parameter Synchronous optimization Based on a Hybrid Intelligent optimization algorithm

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HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2023年 13卷

作者： Wang, Qingjun Mu, Zhendong Shenyang Aerosp Univ Coll Econom & Management Shenyang Peoples R China Nanjing Univ Aeronut & Astronaut Nanjing Peoples R China Jiangxi Univ Technol Ctr Collaborat & Innovat Nanchang Peoples R China

Amid the rapid development of science and technology and the increasingly fierce market competition, low cost coupled with high performance is the key to a company's competitiveness. This requires optimizing all the links in the actual output. Hybrid intelligent algorithms can combine the advantages of different algorithms to solve a large number of optimization problems in engineering practice. Therefore, this study is focused on a hybrid swarm intelligence algorithm and its application. As a classic method of machine learning, the kernel function, which is based on the support vector machine (SVM) and the selection of the parameters in the kernel function, has an important influence on the performance of the classifier. The use of kernel function technology cannot only greatly reduce the amount of calculation in the input space, but can also effectively improve machine learning classification performance. In the field of machine learning, choosing and building the core functions is a notable difficulty. However, little research has been conducted in this area so far. In view of the above problems, this study discusses and analyzes the structure of the support frame machine core in detail, and improves the traditional parameter optimization algorithm. It also proposes a new method of fuzzy clustering algorithm automatic parameter learning combined with the basic ideas of a genetic algorithm in order to improve the parameter optimization strategy of support vector regression, so as to obtain better prediction results. Through simulation experiments, the improved hybrid core SVM and parameter optimization algorithm were applied to the ORL face database, greatly improving the recognition rate, and experiments were carried out after adding noise to the images in the face database to verify the practicability and practicality of the algorithm. The robustness and reliability of the algorithm were improved by at least 30%, thus confirming the feasibility of the proposed a

关键词： Hybrid Intelligence optimization algorithm Feature Selection Parameter Synchronization optimization

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An innovative ensemble model based on multiple neural networks and a novel heuristic optimization algorithm for COVID-19 forecasting

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 212卷 118746页

作者： Qu, Zongxi Li, Yutong Jiang, Xia Niu, Chunhua Lanzhou Univ Sch Management Lanzhou 730000 Peoples R China Lanzhou Univ Res Ctr Emergency Management Lanzhou 730000 Peoples R China Northwest Minzu Univ Affiliated Hosp Prov Peoples Hosp Gansu 2 Lanzhou 730099 Peoples R China

During the global fight against the novel coronavirus pneumonia (COVID-19) epidemic, accurate outbreak trend forecasting has become vital for outbreak prevention and control. Effective COVID-19 outbreak trend prediction remains a complex and challenging issue owing to the significant fluctuations in the COVID-19 data series. Most previous studies have limitations only using individual forecasting methods for outbreak modeling, ignoring the combination of the advantages of different prediction methods, which may lead to insufficient results. Therefore, this paper develops a novel ensemble paradigm based on multiple neural networks and a novel heuristic opti-mization algorithm. First, a new hybrid sine cosine algorithm-whale optimization algorithm (SCWOA) is exer-cised on 15 benchmark tests. Second, four neural networks are used as predictors for the COVID-19 outbreak forecasting. Each predictor is given a weight, and the proposed SCWOA is used to optimize the best matching weights of the ensemble model. The daily COVID-19 series collected from three of the most-affected countries were taken as the test cases. The experimental results demonstrate that different neural network models have different performances in various complex epidemic prediction scenarios. The SCWOA-based ensemble model can outperform all comparable models with its high accuracy and robustness.

关键词： COVID-19 forecasting Ensemble forecasting Neural network optimization algorithm

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