检索结果-内蒙古大学图书馆

Optimization of Capacity Configuration of Wind-Solar-Diesel-Storage Using Improved sparrow search algorithm

JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2022年第1期17卷 1-14页

作者： Dong, Jun Dou, Zhenhai Si, Shuqian Wang, Zichen Liu, Lianxin Shandong Univ Technol SDUT Jinan Shandong Peoples R China Inner Mongolia Univ Sci & Technol IMUST Baotou Inner Mongolia Peoples R China China Agr Univ CAU Beijing Peoples R China

In order to reasonably allocate the capacity of distributed generation and realize the goal of stable, economic and clean operation of the system, a multi-objective optimization model with investment cost, environmental protection and power supply quality as indicators has been established, and the multi-objective sparrow search algorithm is used to optimize the solution. Although the multi-objective search algorithm is more efficient than the traditional single objective algorithm, it is easy to fall into local optimum. To this end, the niche optimization technology is used to improve the optimization effect of multi-objective sparrow search algorithm, and the Levy flight strategy is introduced to enhance the ability of multi-objective sparrow search algorithm to jump out of local optimum. The calculation example uses the traditional multi-object search algorithm and the niche multi-objective sparrow search algorithm with levy disturbance to solve the proposed model. The simulation results verify the effectiveness of the multi-objective sparrow search algorithm improved by levy disturbance and niche optimization technology.

关键词： Capacity configuration Niche optimization technology sparrow search algorithm

来源：评论

学校读者我要写书评

暂无评论

A Robustness Temperature Inversion Method for Cable Straight Joints Based on Improved sparrow search algorithm Optimized BPNN

引用

IEEE ACCESS 2022年 10卷 100137-100150页

作者： Zhan, Qinghua Ruan, Jiangjun Zhu, Hesheng Wang, Yuli Wuhan Univ Sch Elect & Automat Wuhan 430072 Peoples R China China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443000 Peoples R China

The temperature of cable conductor is of great significance to improve the current carrying capacity, asset utilization and safe operation of cable lines. Aiming at the problems of slow calculation speed, low accuracy and weak anti-interference ability of the current temperature calculation methods, this paper proposes an inversion method based on improved sparrow search algorithm (ISSA) optimized back propagation neural network (BPNN). Tent mapping was used to increase the initial population diversity of sparrow. Modified sparrow optimization formula to improve convergence speed. Chaotic perturbation is applied to the optimal individual to improve the global and local search ability of SSA. The multi-physics simulation model of 110kv straight connector was established, and the temperature distribution data under three different working conditions were obtained. According to the simulation data and CEC2017 standard test experiments, the optimization ability of the improved model is compared with particle swarm optimization (PSO), whale optimization algorithm (WOA), SSA and MSWOA. To verify the generalization performance and migration ability of the proposed method, the thermal cycle test and inversion calculation of the 10kV cable straight-through joint were carried out. The results show that ISSA-BPNN has high accuracy, fast convergence speed, good robustness, and is less affected by cable joint type, load current and cable environment conditions. It has good engineering practicability.

关键词： Power cables Temperature measurement Heuristic algorithms Prediction algorithms Optimization Mathematical models Temperature distribution sparrow search algorithm BP neural network tent chaos cable straight joint temperature inversion

来源：评论

学校读者我要写书评

暂无评论

Household PV Power Scheduling Based on an Improved sparrow search algorithm 15

Household PV Power Scheduling Based on an Improved Sparrow S...

引用

15th International Conference on Advanced Computational Intelligence (ICACI)

作者： Lan, Yong -Jun Hu, Zhao-Long Zeng, Ling-Guo Li, Minglu Zhejiang Normal Univ Coll Comp Sci & Technol Jinhua 321004 Zhejiang Peoples R China Zhejiang Normal Univ Coll Phys & Elect Informat Engn Jinhua 321004 Zhejiang Peoples R China

ISBN: (纸本)9798350321456

With renewable energy development, wind and photovoltaic power generation will gradually penetrate the traditional distribution network and occupy a more important part of the distribution network To make up for the shortage of solar power generation, an important measure is to install a household photovoltaic power generation system at the user end, which is an essential part of the future distribution network structure. Users who install PV in their homes will not only be self-sufficient in electricity but will also be able to sell excess power back to the grid. This kind of distributed power directly integrated into the grid without dispatch can cause severe instability to the grid. Based on this, we propose a power dispatching model in this paper, which takes renewable energy and household photovoltaic power generation as the primary power sources of the grid. Household photovoltaic users can sell excess power to the grid while ensuring power consumption. In scheduling, we use an improved sparrow search algorithm to publish scheduling tasks based on real-time power balance so that each generator can provide the most appropriate power. By comparing the running results of the algorithm with different scheduling times and the number of prosumers, we found that when the scheduling time is different, the total cost does not change. When the number of prosumers increases, the total cost will decrease, and the efficiency of the improved algorithm remains stable.

关键词： renewable energy power dispatching household photovoltaic sparrow search algorithm

来源：评论

学校读者我要写书评

暂无评论

Historical Location Information Based Improved sparrow search algorithm for Microgrid Optimal Dispatching 18th

Historical Location Information Based Improved Sparrow Searc...

引用

18th International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA)

作者： Zhou, Ting Shen, Bo Pan, Anqi Xue, Jiankai Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Minist Educ Engn Res Ctr Digitalized Text & Fash Technol Shanghai 201620 Peoples R China

ISBN: (纸本)9789819722747;9789819722754

The sparrow search algorithm (SSA), as an efficient metaheuristic algorithm, has been widely used on practical problems in various fields. Nevertheless, the basic SSA is prone to fall into local optimum, which weakens the optimization ability. In order to address this problem, a novel improved SSA, called the historical location information based sparrow search algorithm (HLI-SSA), is presented. In order to solve the problem that the original sparrow search algorithm will miss part of the information during the iteration process, the historical useful information is fully utilized by creating a memory bank, which can make more population information available to individual sparrows. In addition, the L ' evy stable distribution strategy is applied to improve the ability of jumping out of the local optimum. The adaptive quadratic interpolation mechanism and the use of randomness are introduced to enhance the algorithm diversity. The proposed HLI-SSA is then validated on the CEC benchmark functions. The experimental results indicate that the HLI-SSA can improve the optimization performance of the basic SSA. Finally, the method is successfully employed to the microgrid optimal dispatching problem under extreme conditions.

关键词： sparrow search algorithm memory base Levy stabilized distribution adaptive quadratic interpolation mechanism microgrid optimal dispatching

来源：评论

学校读者我要写书评

暂无评论

Data Augmentation for Small Sample Iris Image Based on a Modified sparrow search algorithm

引用

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2022年第1期15卷 1-11页

作者： Xiong, Qi Zhang, Xinman He, Shaobo Shen, Jun Hunan Univ Arts & Sci Int Collage Changde 415000 Hunan Peoples R China Xi An Jiao Tong Univ Fac Elect & Informat Engn Sch Automat Sci & Engn MOE Key Lab Intelligent Networks & Network Secur Xian 710049 Shanxi Peoples R China Cent South Univ Sch Phys & Elect Changsha 410083 Hunan Peoples R China Univ Wollongong Sch Comp & Informat Technol Wollongong NSW 2522 Australia

Training convolutional neural networks (CNN) often require a large amount of data. However, for some biometric data, such as fingerprints and iris, it is often difficult to obtain a large amount of data due to privacy issues. Therefore, training the CNN model often suffers from specific problems, such as overfitting, low accuracy, poor generalization ability, etc. To solve them, we propose a novel image augmentation algorithm for small sample iris image in this article. It is based on a modified sparrow search algorithm (SSA) called chaotic Pareto sparrow search algorithm (CPSSA), combined with contrast limited adaptive histogram equalization (CLAHE). The CPSSA is used to search for a group of clipping limit values. Then a set of iris images that satisfies the constraint condition is produced by CLAHE. In the fitness function, cosine similarity is used to ensure that the generated images are in the same class as the original one. We select 200 categories of iris images from the CASIA-Iris-Thousand dataset and test the proposed augmentation method on four CNN models. The experimental results show that, compared with the some standard image augmentation methods such as flipping, mirroring and clipping, the accuracy and Equal Error Rate (EER)of the proposed method have been significantly improved. The accuracy and EER of the CNN models with the best recognition performance can reach 95.5 and 0.6809 respectively. This fully shows that the data augmentation method proposed in this paper is effective and quite simple to implement.

关键词： Data augmentation Small sample Swarm intelligence sparrow search algorithm Iris images

来源：评论

学校读者我要写书评

暂无评论

Optimal energy management of micro-grid using sparrow search algorithm

引用

ENERGY REPORTS 2022年 8卷 758-773页

作者： Fathy, Ahmed Alanazi, Turki M. Rezk, Hegazy Yousri, Dalia Jouf Univ Fac Engn Elect Engn Dept Sakakah Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Engn Wadi Addawaser Al Kharj Saudi Arabia Minia Univ Fac Engn Elect Engn Dept Al Minya Egypt Fayoum Univ Fac Engn Elect Engn Dept Al Fayyum Egypt

In this paper, a recent metaheuristic approach of sparrow search algorithm (SSA) is employed to manage the operation of microgrid (MG) in an optimal manner. Two optimization problems are formulated in this work, the first one is a single objective problem that aims at minimizing either the total operating cost or the total emission from the system. The second problem is multi objective comprising both total operating cost and total emission simultaneously. A new version of SSA is proposed to manage the energy of MG optimally. The considered MG comprises photovoltaic modules (PV), wind turbine (WT), fuel cell (FC), micro-turbine (MT), batteries (BSS), and grid. The obtained results using the proposed SSA are compared to other programmed approaches of krill herd optimizer (KH), Harris hawks optimizer (HHO), artificial ecosystem-based optimizer (AEO), Fuzzy self adaptive particle swarm optimization (FSAPSO), and antlion optimizer (ALO). Non-parametric analysis using Friedman and Kruskal-Wallis ANOVA tests are applied for evaluating the proposed algorithm performance statistically. At solving the single objective problem, the proposed SSA achieved minimizing in cost and emission with 1.44% and 54.76% respectively compared to KH. Regarding the multi-objective problem, the proposed multi-objective SSA (MOSSA) succeeded in saving about 42.78% in operating cost and reducing the emission by 0.118% compared to ALO. The main findings demonstrated the robustness of the proposed SSA in managing the operation of the constructed MG.(c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Microgrid sparrow search algorithm Energy management Optimization

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Modeling Method for Aluminum Smelting Process Based on a Hybrid Strategy-Based sparrow search algorithm

引用

IEEE ACCESS 2022年 10卷 101149-101159页

作者： Luo, Yasong Dai, Jiayang Chen, Xingyu Liu, Yilin Guangxi Univ Sch Elect Engn Guangxi Key Lab Intelligent Control & Maintenance Nanning Peoples R China

In the production of aluminum, the regenerative aluminum smelting process is an important part for energy efficiency and product quality. Aluminum liquid temperature is a significant variable in the aluminum smelting process, and it is costly to measure timely because it requires protective temperature sensor. To handle this problem, a kind of modeling framework which combine a mechanism model with multi-scale kernel technique is proposed. First, the mechanism model is built for the aluminum liquid temperature by the energy conservation law and heat transfer mechanism. Since the mechanism model is based on some assumptions, it often results in unknown variables. Thus, the multi-scale kernel technique is used to obtain the unknown variables. Finally, a hybrid temperature prediction model is built by combining the multi-scale kernel and the mechanism model. The parameter identification of the hybrid model is described as an optimization problem, and a hybrid strategy-based sparrow search algorithm (HSSA) is proposed to solve this problem. The experiment results show that HSSA has higher convergence accuracy and stronger global search ability than the original sparrow search algorithm (SSA), and the proposed hybrid model can correctly estimate the aluminum liquid temperature.

关键词： Aluminum Smelting Furnaces Production processes Optimization Predictive models Prediction algorithms Regenerative aluminum smelting mechanism modeling multi-scale kernel sparrow search algorithm

来源：评论

学校读者我要写书评

暂无评论

Design and application of improved sparrow search algorithm based on sine cosine and firefly perturbation

引用

MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第11期19卷 11422-11452页

作者： Ren, Xiangyang Chen, Shuai Wang, Kunyuan Tan, Juan Hebei Univ Engn Sch Management Engn & Business Handan 056038 Peoples R China Hebei Univ Engn Sch Marxism Handan 056038 Peoples R China

Swarm intelligence algorithms are relatively simple and highly applicable algorithms, especially for solving optimization problems with high reentrancy, high stochasticity, large scale, multi-objective and multi-constraint characteristics. The sparrow search algorithm (SSA) is a kind of swarm intelligence algorithm with strong search capability, but SSA has the drawback of easily falling into local optimum in the iterative process. Therefore, a sine cosine and firefly perturbed sparrow search algorithm (SFSSA) is proposed for addressing this deficiency. Firstly, the Tent chaos mapping is invoked in the initialization population stage to improve the population diversity;secondly, the positive cosine algorithm incorporating random inertia weights is introduced in the discoverer position update, so as to improve the probability of the algorithm jumping out of the local optimum and speed up the convergence;finally, the firefly perturbation is used to firefly perturb the sparrows, and all sparrows are updated with the optimal sparrows using the firefly perturbation method to improve their search-ability. Thirteen benchmark test functions were chosen to evaluate SFSSA, and the results were compared to those computed by existing swarm intelligence algorithms, as well as the proposed method was submitted to the Wilcoxon rank sum test. Furthermore, the aforesaid methods were evaluated in the CEC 2017 test functions to further validate the optimization efficiency of the algorithm when the optimal solution is not zero. The findings show that SFSSA is more favorable in terms of algorithm performance, and the method's searchability is boosted. Finally, the suggested algorithm is used to the locating problem of emergency material distribution centers to further validate the feasibility and efficacy of SFSSA.

关键词： sparrow search algorithm Tent chaotic mapping positive cosine algorithm perturbation strategy firefly perturbation strategy

来源：评论

学校读者我要写书评

暂无评论

Hydrological time series prediction by extreme machine learning and sparrow search algorithm

引用

WATER SUPPLY 2022年第3期22卷 3143-3157页

作者： Feng, Bao-fei Xu, Yin-shan Zhang, Tao Zhang, Xiao Changjiang Water Resources Commiss Bur Hydrol Wuhan 430010 Peoples R China

In general, accurate hydrological time series prediction information is of great significance for the rational planning and management of water resource system. Extreme learning machine (ELM) is an effective tool proposed for the single-layer feedforward neural network in the regression and classification problems. However, the standard ELM model falls into local minimum with a high probability in hydrological prediction problems since the randomly assigned parameters (like input-hidden weights and hidden biases) often remain unchanged at the learning process. For effectively improving the prediction accuracy, this paper develops a hybrid hydrological forecasting model where the emerging sparrow search algorithm (SSA) is firstly used to determine the satisfying parameter combinations of the ELM model, and then the Moore-Penrose generalized inverse method is chosen to analytically obtain the weight matrix between the hidden layer and output layer. The proposed method is used to forecast the long-term daily runoff series collected from three real-world hydrological stations in China. Based on several performance evaluation indexes, the results show that the proposed method outperforms several ELM variants optimized by other evolutionary algorithms in both training and testing phases. Hence, an effective evolutionary machine learning tool is developed for accurate hydrological time series forecasting.

关键词： extreme machine learning hydrologic forecasting sparrow search algorithm

来源：评论

学校读者我要写书评

暂无评论

Spectral Reflectance Reconstruction Based on BP Neural Network and the Improved sparrow search algorithm

引用

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2022年第8期E105卷 1175-1179页

作者： Zhang, Lu Wang, Chengqun Fang, Mengyuan Xu, Weiqiang Zhejiang Sci Tech Univ Zhejiang Engn Lab Ind Internet Commun Technol Hangzhou 310018 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China

To solve the problem of metamerism in the color reproduction process, various spectral reflectance reconstruction methods combined with neural network have been proposed in recent years. However, these methods are generally sensitive to initial values and can easily converge to local optimal solutions, especially on small data sets. In this paper, we propose a spectral reflectance reconstruction algorithm based on the Back Propagation Neural Network (BPNN) and an improved sparrow search algorithm (SSA). In this algorithm, to solve the problem that BPNN is sensitive to initial values, we propose to use SSA to initialize BPNN, and we use the sine chaotic mapping to further improve the stability of the algorithm. In the experiment, we tested the proposed algorithm on the X-Rite ColorChecker Classic Mini Chart which contains 24 colors, the results show that the proposed algorithm has significantly better performance compared to other algorithms and moreover it can meet the needs of spectral reflectance reconstruction on small data sets. Code is avaible at https://***/LuraZhang/spectral-reflectance-reconsctuction.

关键词： sparrow search algorithm spectral reflectance reconstruction BPNN SSA-BP

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：