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Research on the Capacity Allocation and Planning Model of a Flexible Direct Current Transmission System Based on swarm intelligence optimization algorithm

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INTERNATIONAL JOURNAL OF WEB SERVICES RESEARCH 2024年第1期21卷 1页

作者： Hao, Weihan Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Technol Wuhan Peoples R China

Based on the contradiction between the increasing power demand and the low capacity of the traditional direct current (DC) transmission system, this study innovatively combines the swarm intelligence optimization algorithm with power transmission and storage, and establishes a flexible DC transmission system model based on the swarm intelligence optimization algorithm. The study also analyzes the difference between the optimized capacity configuration of flexible DC transmission system and the traditional transmission system under different factors. The research results show that the transmission mode has become an important factor affecting the capacity allocation of flexible DC transmission system. The transmission medium and transmission cable type also have an impact on the capacity allocation of flexible DC transmission system. Optimizing power transmission mode, changing transmission medium and whether to add the swarm intelligence optimization algorithm are of far-reaching significance for improving the capacity allocation of flexible DC transmission system.

关键词： swarm intelligence optimization algorithm Artificial intelligence Flexible DC Transmission Capacity Configuration Energy Loss

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UAV Task Allocation Method Using swarm intelligence optimization algorithm 15th

UAV Task Allocation Method Using Swarm Intelligence Optimiza...

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15th China Conference on Wireless Sensor Networks (CWSN)

作者： Shi, Jiaqi Tan, Li Lian, Xiaofeng Xu, Tianying Zhang, Hongtao Beijing Technol & Business Univ Sch Comp Sci & Engn Beijing 100048 Peoples R China Beijing Technol & Business Univ Sch Artificial Intelligence Beijing 100048 Peoples R China

ISBN: (纸本)9789811681745;9789811681738

The multi-unmanned aerial vehicle (UAV) task allocation method has shortcomings such as long flight distance and long algorithm initialization time. In response to these problems, this paper proposes a UAV task allocation method based on swarm intelligence optimization algorithm (SIOA). The algorithm first compares the relationship between the number of UAVs and mission points when UAV is performing a task, and then introduces the idea of gradient descent to reduce the flying distance of UAV. Experimental results show that the SIOA method can effectively reduce the initialization time, shorten search distance of the UAV and the time UAV complete the task, and effectively solve the problem of high algorithm complexity.

关键词： Task allocation swarm intelligence optimization algorithm algorithm complexity Initialization time Gradient descent

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Construction of Stomatology Development Review System Based on swarm intelligence optimization algorithm 22

Construction of Stomatology Development Review System Based ...

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Proceedings of the 2022 5th International Conference on Machine Learning and Machine intelligence

作者： Qian Zhang Xin Peng Guizhou University Humanities and Medical Research Center Zunyi Medical University China

ISBN: (纸本)9781450397551

After the human society entered the network age, with the continuous development of information technology, the amount of data generated and collected in various production activities continued to increase, which prompted a new change in the field of machine learning and data mining research. swarm intelligence optimization algorithm is an important research direction in the field of evolutionary computing. Based on the theoretical research of the swarm intelligence optimization algorithm, this paper studies and analyzes the construction of the stomatology development review system based on the swarm intelligence optimization algorithm, and uses its optimization performance to deal with the feature selection application of medical data sets. The swarm intelligence optimization algorithm has the characteristics of simple structure and high performance, and is widely used in the field of optimization problems. Use a given optimization algorithm to find the best set of features. The main purpose is to find the feature set with the strongest correlation with the prediction result. In this way, the efficiency of processing the dataset can be improved, the training of the machine learning model can be accelerated, and finally the classification ability of the entire combined model can be improved. In this paper, the improved swarm intelligence optimization algorithm based on multi-swarm mechanism is used to solve the feature selection problem of medical data sets, which provides practical application help for the construction of the stomatology development review system. The final experimental results show that when the nonlinear coefficients of the system are 47.9, 16.3, 36.5, 79.3 and 60.2, respectively, the convergence degrees of the corresponding nonlinear convergence factors of the system are 77.3%, 80.5%, 78.7%, 75.1% and 78.4, respectively. It shows that the construction of a review system for the development of stomatology based on the swarm intelligence optimizat

关键词： Development Review System Stomatology swarm intelligence optimization algorithm System Construction

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Virtual Screening and Discovery of Matrix Metalloproteinase-12 Inhibitors by swarm intelligence optimization algorithm-Based Machine Learning

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CHEMISTRYSELECT 2020年第36期5卷 11112-11119页

作者： Zhang, Chenghua Wu, Keliang Huang, Long Sun, Kenan Zou, Yurong Xiong, Zhihui Li, Bingke North Sichuan Med Coll Sch Basic Med Sci Nanchong 637007 Peoples R China Nanyang Inst Technol Sch Biol & Chem Engn Henan Key Lab Ind Microbial Resources & Fermentat Nanyang 473004 Peoples R China China Natl Nucl Corp Res Inst 280 Deyang 618300 Guanghan Peoples R China Chengdu Normal Univ Coll Chem & Life Sci Chengdu 611130 Peoples R China Chengdu Normal Univ Coll Phys & Engn Chengdu 611130 Peoples R China

Matrix metalloproteinase-12 (MMP-12) is an attractive therapeutic target for drug design and discovery for many human conditions. In this study, six swarm intelligence optimization algorithms were applied to optimize the parameters of the model generated using the LibSVM toolkit in MATLAB to identify potential MMP-12 inhibitors (MMP-12is);six types of optimized support vector machine (SVM) models were established. The highest prediction accuracy obtained was 98.89 %, which was equivalent to the effect of the optimal "RF+opt" model. All six models passed the Y-randomization test and showed excellent performance with reliable results. Virtual screening identified 371 molecules with a predictive probability score greater than 0.9. The optimized SVM models, in addition to "RF+opt" and "SVM2" models, were combined to establish a consistency evaluation system. Our results revealed six non-toxic potential MMP-12is. This process provides a strong theoretical basis for the design, synthesis, and development of novel drugs targeting MMP-12.

关键词： Inhibitors Virtual Screening swarm intelligence optimization algorithm Consistency Evaluation Machine Learning Molecular Docking

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Combined Hybrid Neural Networks and swarm intelligence optimization algorithms for Photovoltaic Panel Segmentation From Remote Sensing Images

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IEEE ACCESS 2024年 12卷 75941-75950页

作者： Zhang, Xiaoqing Qi, Qingqing Liu, Weike Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Shandong Peoples R China Chinese Acad Sci Qingdao Inst Bioenergy & Bioproc Technol Extended Energy Big Data & Strategy Res Ctr Qingdao 266104 Peoples R China Shandong Energy Inst Qingdao 266101 Peoples R China Qingdao New Energy Shandong Lab Qingdao 266101 Peoples R China Shandong Univ Sci & Technol Ctr Informat & Network Qingdao 266590 Shandong Peoples R China

In the context of traditional energy shortage and climate warming, the development of solar energy, as a clean and renewable energy, is crucial. As an effective way to utilize solar energy resources, photovoltaic (PV) power generation technology has been widely used around the world. Using remote sensing images to extract PV panel information, including location, area, has a positive effect on understanding the development status, planning and construction of regional PV new energy. In this study, a semantic segmentation network called HCT-Net, combined with the hybrid neural networks and the swarm intelligence optimization algorithms, is designed to segment solar PV panels from remote sensing images automatically and accurately. To address the problem of inconsistent segmentation within PV regions, a hybrid encoder, which combines a convolutional neural network and a Transformer, is designed to extract local features with rich detail information and global features with global context dependencies, resulting in enhanced feature representations. The foreground relation module is designed to solve the problem of mis-segmentation of the background into PVs. This module strengthens the model's focus on the target object and suppresses the feature representations of non-PVs by explicitly learning the similarity relationship between the global PV feature representation and the feature representations of other objects, and by adaptively assigning weights according to the similarity. The swarm intelligence optimization algorithm is applied to adjust the learning rate and the balance coefficient of the composite loss function of HCT-Net during training. Experimental results show that compared with the current mainstream semantic segmentation network, the method in this study effectively alleviates the problem of inconsistent segmentation within PV regions and mis-segmentation and has advantages in the complete and accurate extraction of PV panels.

关键词： Feature extraction optimization Particle swarm optimization Transformers Semantics Photovoltaic systems Remote sensing Semantic segmentation Convolutional neural networks Photovoltaic panel extraction remote sensing image semantic segmentation swarm intelligence optimization algorithm CNN transformer

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Using swarm intelligence optimization algorithms to predict the height of fractured water-conducting zone

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ENERGY EXPLORATION & EXPLOITATION 2023年第5期41卷 1603-1627页

作者： Zhao, Dekang Li, Zhenghao Feng, Guorui Wang, Fulong Hao, Chenwei He, Yaming Dong, Shuning Taiyuan Univ Technol Coll Min Engn Taiyuan Peoples R China China Coal Technol & Engn Grp Corp Xian Res Inst Co Ltd Xian Peoples R China Shandong Univ Sch Qilu Transportat Jinan Peoples R China Key Lab Shanxi Prov Mine Rock Strata Control & Dis Taiyuan Peoples R China Shanxi Prov Res Ctr Green Min Engn Technol Taiyuan Peoples R China Changzhi Xinjian Coal Ind Co Ltd Changzhi Peoples R China China Coal Technol & Engn Grp Corp Xian Res Inst Co Ltd Xian 710054 Peoples R China

The accurate calculation of the height of fractured water-conducting zone (FWCZ) is of great significance for mine optimization design, water disaster prevention, and safety production of the coal mines. In this article, a height-prediction model of FWCZ based on extreme learning machine (ELM) is proposed. To address the issues of low prediction accuracy and challenging parameter optimization, we optimized the ELM model using the gray-wolf optimization algorithm (GOA), whale optimization algorithm (WOA), and salp optimization algorithm (SOA). These optimization algorithms mitigate the issues of slow convergence, poor stability, and local optimality associated with traditional neural networks. The mining depth, mining height, overburden strata structure, working face length, and coal seam dip angle are selected as the main controlling factors for the height of FWCZ. A total of 42 fields-measured samples are collected and divided into 2 subsets for training and validating with a ratio of 36/6. The prediction capability of GOA-ELM, WOA-ELM, and SOA-ELM models are evaluated and compared, and the results show that the calculation results of the three models are optimized compared with the ELM model. The prediction capability of GOA and WOA are similar, while the prediction results of SOA-ELM are better than the other two models, and the relative errors of the test sets are all less than 10%. Therefore, the SOA-ELM model is finally applied to predict the height of FWCZ formed after the mining of No.15 coal seam in Xinjian Coal Mine. Finally, we verified the prediction results using measured data from the borehole television detection instrument, which showed good consistency. This provides further evidence of the effectiveness of the swarm intelligence optimization algorithm in predicting the height of FWCZ.

关键词： FWCZ swarm intelligence optimization algorithm GOA WOA SOA

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Analysis of the influence of population distribution characteristics on swarm intelligence optimization algorithms

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INFORMATION SCIENCES 2023年第1期645卷

作者： Hu, Rongxin Bao, Liyong Ding, Hongwei Zhou, Dongmin Kong, Yan Yunnan Univ Sch Informat Sci & Engn Kunming 650000 Peoples R China

The distribution characteristic of populations is one of the main characteristics of population. The pattern of distribution characteristics determines the probability law of individual values. At the same time, the initialization of the population lays a foundation for the iterative process of the swarm intelligence optimization algorithm. To reveal the influence of population distribution characteristics on the swarm intelligence optimization algorithm, this paper proposes a variety of search strategies based on the populations with different distribution characteristics and analyzes the influence of population distribution characteristics on optimization process by comparing the test results of the optimization algorithm after implementing these strategies. First, a new population generator is designed that can transform the same initial population into a population with uniform and central peaking distributions. On this basis, the two kinds of populations are applied to the global and local search stages of the optimization process, and four different search strategies are formed. Among them, the global and local search strategies based on a uniformly distributed population are the traditional methods. Finally, the performance of the optimization algorithm using different search strategies is evaluated through 29 test problems from the CEC2017 benchmark for 10, 30, 50 and 100 dimensions. In addition, the algorithms are applied to solve the TSP problem. For CEC2017 in 100D, only 13 of the 29 test functions achieve the best optimization effect by using the traditional method, while the other 16 test functions achieve better search results by using the other three search strategies. The analysis shows that the population distribution characteristics have a great influence on the population optimization algorithm. The performance of the algorithm with different population distribution combination strategies is statistically superior to the traditional algorithm with

关键词： Population distribution characteristics swarm intelligence optimization algorithm Chaotic mapping CEC2017 TSP

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Product quality prediction based on RBF optimized by firefly algorithm

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Journal of Systems Engineering and Electronics 2024年第1期35卷 105-117页

作者： HAN Huihui WANG Jian CHEN Sen YAN Manting College of Electronics and Information Engineering Tongji UniversityShanghai 201804China

With the development of information technology,a large number of product quality data in the entire manufacturing process is accumulated,but it is not explored and used *** traditional product quality prediction models have many disadvantages,such as high complexity and low *** overcome the above problems,we propose an optimized data equalization method to pre-process dataset and design a simple but effective product quality prediction model:radial basis function model optimized by the firefly algorithm with Levy flight mechanism(RBFFALM).First,the new data equalization method is introduced to pre-process the dataset,which reduces the dimension of the data,removes redundant features,and improves the data *** the RBFFALFM is used to predict product *** expe riments conducted on real-world product quality datasets validate that the new model RBFFALFM combining with the new data pre-processing method outperforms other previous me thods on predicting product quality.

关键词： product quality prediction data pre-processing radial basis function swarm intelligence optimization algorithm

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CGAOA-STRA-BiConvLSTM: An automated deep learning framework for global TEC map prediction

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GPS SOLUTIONS 2025年第1期29卷 1-28页

作者： Liu, Haijun Wang, Haoran Le, Huijun Yuan, Jing Shan, Weifeng Wu, Yang Chen, Yi Inst Intelligent Emergency Informat Proc Inst Disaster Prevent Langfang 065201 Peoples R China Chinese Acad Sci Inst Geol & Geophys Key Lab Earth & Planetary Phys Beijing 100029 Peoples R China Inst Disaster Prevent Sch Informat Engn Langfang 065201 Peoples R China

Global ionospheric total electron content (TEC) map prediction is important for improving the accuracy of global navigation satellite systems. There are two main issues with the current TEC prediction: (1) The deep learning models used for TEC prediction are mainly designed using a stacked structure. When stacking multiple layers, the input data will undergo continuous multi-layer convolution operations, leading to the loss of fine-grained features and the degradation of model performance;(2) The model optimization methods for TEC prediction are relatively outdated, mainly using manual optimization or grid search methods. To address these two issues, an automatic framework for global TEC map prediction and optimization is proposed, named as CGAOA-STRA-BiConvLSTM. It includes a global TEC map prediction model, STRA-BiConvLSTM, which can simultaneously extract both coarse-grained and fine-grained spatiotemporal features. It also contains an optimization algorithm, CGAOA, to optimize the model. We first experimentally verified the effectiveness of CGAOA. Then, the effectiveness of STRA-BiConvLSTM was verified through ablation experiments. Finally, we conducted comparative experiments from multiple perspectives between our framework and 5 mainstream methods: C1PG, C2PG, ConvLSTM, ConvGRU, and ED-ConvLSTM. The results show that in all cases, the proposed CGAOA-STRA-BiConvLSTM outperforms the comparative models.

关键词： Total electron content (TEC) Spatio-temporal residual attention (STRA) swarm intelligence optimization algorithm Arithmetic optimization algorithm (AOA) Spatiotemporal forecasting

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A multiple surrogate-assisted hybrid evolutionary feature selection algorithm

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swarm AND EVOLUTIONARY COMPUTATION 2025年 92卷

作者： Zhang, Wan-qiu Hu, Ying Zhang, Yong Zheng, Zi-wang Peng, Chao Song, Xianfang Gong, Dunwei China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Peoples R China Anhui Normal Univ Sch Comp & Informat Wuhu 241000 Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266100 Peoples R China

Feature selection (FS) is an important data processing technology. However, existing FS methods based on evolutionary computation have still the problems of "curse of dimensionality"and high computational cost, with the increase of the number of feature and/or the size of instance. In view of this, the paper proposes a multiple surrogate-assisted hybrid evolutionary feature selection (MSa-HEFS). Two kinds of surrogates (i.e., objective regression surrogate and sample surrogate) and two kinds of FS methods (i.e., filter and wrapper) are integrated into MSa-HEFS to improve its performance. Firstly, an ensemble filter FS method is designed to reduce the search space of subsequent wrapper evolutionary FS method. Secondly, in the proposed evolutionary FS method, a dual-surrogate-assisted hierarchical individual evaluation mechanism is developed to reduce the evaluation cost on feature subsets, an online management and update strategy is used to adaptively choose appropriate surrogates for individuals. The proposed algorithm is applied to 12 typical datasets and compared with 4 state-of-the-art FS algorithms. Experimental results show that MSa-HEFS can obtain good feature subsets at the smallest computational cost on all datasets. MSa-HEFS source code is available on Github at https://***/ZZW-zq/MSa-HEFS-/tree/master.

关键词： Brain storming optimization swarm intelligence optimization algorithm Feature selection Surrogate-assisted evolutionary algorithm

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