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A comprehensive survey of the application of swarm intelligent optimization algorithm in photovoltaic energy storage systems

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SCIENTIFIC REPORTS 2024年第1期14卷 1-30页

作者： Wang, Shuxin Yue, Yinggao Cai, Shaotang Li, Xiaojuan Chen, Changzu Zhao, Hongliang Li, Tiejun Shanghai Zhongqiao Vocat & Tech Univ Sch Intelligent Mfg Shanghai 201514 Peoples R China Wenzhou Univ Technol Sch Intelligent Mfg & Elect Engn Wenzhou 325035 Peoples R China State Grid Chongqing Elect Power Co Informat & Commun Branch Chongqing 400014 Peoples R China Zhejiang Carspa New Energy LTD Wenzhou 325035 Peoples R China

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 optimization algorithms 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.

关键词： Photovoltaic energy storage system Battery energy storage Optimal scheduling swarm intelligence optimization algorithm Application

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FRACTIONAL ORDER CHAOTIC MODEL BASED ENHANCED EQUILIBRIUM optimization algorithm FOR CONTROLLER DESIGN OF 3 DOF HOVER FLIGHT SYSTEM 17

FRACTIONAL ORDER CHAOTIC MODEL BASED ENHANCED EQUILIBRIUM OP...

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ASME International Design Engineering Technical Conferences / 41st Annual Computers and Information in Engineering Conference (IDETC-CIE) / 17th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)

作者： Ates, Abdullah Chen, YangQuan Inonu Univ Dept Comp Engn Malatya Turkey UC Merced Mechatron Embedded Syst & Automat Lab MESALab Merced CA USA

ISBN: (纸本)9780791885437

In this study, the K feedback gain vector parameters that are used for the control of three degree of freedom four rotor quadcopter system (3 DOF Hover) are optimized with the Enhanced Equilibrium optimization algorithm ((EO)-O-2). The (EO)-O-2 algorithm is proposed with using parameters obtained from fractional order chaotic oscillator models instead of random variables. The Basic EO algorithm is inspired by volume- mass balance. In EO algorithm, each particle is called a motion that searches a parameter vector space. However, random coefficients derived from uniform distribution are used in the parameters updating process or in the generation of the initial population. The (EO)-O-2 algorithm was proposed by using vectors obtained from fractional order chaotic oscillators instead of stochastic coefficients in the basic Equilibrium optimization algorithm. Genesio Tesi, Rossler, Lotka Volterra fractional-order chaotic oscillator models were used in the (EO)-O-2 algorithm to optimize K feedback gain vector of 3 DOF Hover. The order and initial conditions the fractional chaotic oscillator models were experimentally adjusted for the control of 3 DOF problem. Thus, suitable fractional-order chaotic models for the problem were obtained. The (EO)-O-2 algorithm results are compared with the Stochastic Multi Parameter optimization (SMDO) and Discreet Stochastic optimization (DSO) algorithms for the system's pitch, roll and yaw angles.

关键词： Fractional Calculus swarm intelligence optimization algorithm Control

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optimization algorithm of Spotted Hyena Based on Chaotic Reverse Learning Strategy 22

Optimization Algorithm of Spotted Hyena Based on Chaotic Rev...

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Proceedings of the 2022 5th International Conference on Artificial intelligence and Pattern Recognition

作者： Xu He Hengzhi Lu Zixing Ling School of Artificial Intelligence Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450396899

The application of swarm optimization algorithm in WSNs has become a new research hotspot of scholars at home and abroad. Aiming at the problem that the spotted hyena optimization algorithm is easy to fall into local optimum, which leads to low optimization accuracy, an improved spotted hyena optimization algorithm is proposed. On the basis of the original algorithm, Sine chaotic map and elite reverse learning strategy are embedded to reduce the probability of falling into local optimum and improve the global search ability of spotted hyena optimization algorithm. In addition, the adaptive inertia weight is introduced to balance the global search and local development capabilities of the spotted hyena optimization algorithm. The experimental results show that compared with the original spotted hyena optimization algorithm, sine and cosine algorithm, multiverse optimization algorithm, differential evolution algorithm and particle swarm optimization algorithm, the improved algorithm has significant performance advantages in optimization ability and stability.

关键词： Elite reverse learning strategy Inertia weight Sine chaotic mapping Spotted hyena optimization algorithm swarm intelligence optimization algorithm

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Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping

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JOURNAL OF HYDROLOGY 2020年第0期581卷 124379-000页

作者： Quang-Thanh Bui Quoc-Huy Nguyen Xuan Linh Nguyen Vu Dong Pham Huu Duy Nguyen Van-Manh Pham VNU Univ Sci Fac Geog Ctr Appl Res Remote Sensing & GIS CARGIS 334 Nguyen Trai Hanoi Vietnam VNU Univ Sci Fac Geog 334 Nguyen Trai Hanoi Vietnam

This study proposed and compared several novel hybrid models that combined swarm intelligence algorithms and Deep Learning Neural Network for flood susceptibility mapping. Lai Chau, a province in the northwest mountainous region of Vietnam was chosen as a case study since it had recently undergone severe flashflood in 2018. For this purpose, numerical predictor variables such as topographically derived factors (Digital Elevation Model, Aspect, Slope, Curvature, Topographic Wetness Index), climatic variables (Rain), and hydrological variables (stream density, stream power index, distance to river) and multiple remote sensing indices (Normalized Difference Vegetation Index, Normalized Difference Buildup Index) were used. These predictor variables were selected because they are globally collectible and reproducible. The performances of these models were evaluated by using common statistical indicators, namely Root Mean Square Error, Mean Absolute Error, Overall Accuracy and Area under Receiving Operating Characteristics, and the statistical test of differences. The results showed that the proposed swarm intelligence models outperformed benchmarked methods, namely Particle swarm optimization, Support Vector Machine, Random Forest in almost all comparing indicators. It is suggested that proposed models are more robust than the classifiers, which were used for benchmarking and they are good alternatives for flood susceptibility mapping given the availability of dataset.

关键词： swarm intelligence optimization algorithm Deep Learning Neural Network Flash flood

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Flamingo Search algorithm and Its Application to Path Planning Problem 21

Flamingo Search Algorithm and Its Application to Path Planni...

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Proceedings of the 2021 4th International Conference on Artificial intelligence and Pattern Recognition

作者： Zhiheng Wang Jianhua Liu School of Computing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450384087

After studying the migratory and foraging behavior of flamingos, this paper proposes a new swarm intelligence optimization algorithm: flamingos search algorithm. At the same time, in order to verify the optimization effect and stability of FSA, this paper conducts tests and comparisons on 20 different benchmark functions, and standard deviation of the proposed FSA outperformed the other algorithms with regard to 20 test functions and path planning applications. Finally, this paper applies FSA to the path optimization problem, and the test results show that FSA has good engineering application potential, especially in the solution of path planning problems with good optimization performance. Experimental code for this article can be obtained from this website: https://***/18280426650/FSA.

关键词： Constrained optimization Flamingo search algorithm Path planning problem swarm intelligence optimization algorithm

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Research on many-to-many target assignment for unmanned aerial vehicle swarm in three-dimensional scenarios

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COMPUTERS & ELECTRICAL ENGINEERING 2021年 91卷 107067-107067页

作者： Hua, Xiang Wang, Zhao Yao, Hongjuan Li, Baohua Shi, Chenglong Zuo, Jiaxian Xian Technol Univ Sch Def Sci & Technol Xian 710021 Shaanxi Peoples R China Xian Technol Univ Sch Elect Informat Engn Xian 710021 Shaanxi Peoples R China

Target assignment for unmanned aerial vehicle (UAV) swarm has been a research hotspot in academic and industry communities. The current methods mainly focus on multiple targets assignment in planes or few targets assignment in solids. However, they ignore three-dimensional scenarios for UAV swarm and multiple targets characteristics for assignment problem. To solve these issues, we propose a multi-target intelligent assignment model. Firstly, we introduce damage cost and time cost to evaluate the system performance of swarm and time performance in three-dimensional scenarios. Then, we design a bio-inspired swarm intelligence optimization algorithm to find the optimal multiple targets assignment and to balance the two costs and multiple constraints simultaneously. This algorithm regards UAVs as several parallel biological sub-populations, which adopts the multi-layered optimization strategy to select the suitable assignment sequence. The simulation results demonstrate that the proposed method is effective for multi-target assignment in three-dimensional scenarios.

关键词： Unmanned aerial vehicle Target assignment swarm intelligence optimization algorithm Three-dimensional scenario

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Research and application based on the swarm intelligence algorithm and artificial intelligence for wind farm decision system

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RENEWABLE ENERGY 2019年 134卷 681-697页

作者： Zhao, Xuejing Wang, Chen Su, Jinxia Wang, Jianzhou Lanzhou Univ Sch Math & Stat Lanzhou 730000 Gansu Peoples R China Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Gansu Peoples R China Dongbei Univ Finance & Econ Sch Stat Dalian Peoples R China

Wind energy is an increasing concern for wind farm administrators. Effective wind energy potential analysis and accurate forecasting can reduce the operating cost of wind farms. However, many previous studies have been restricted to analyses of wind energy potential analysis and wind speed forecasting, which may result in poor decisions and inaccurate power scheduling for wind farms. This study develops a wind energy decision system based on swarm intelligence optimization and data preprocessing, which includes two modules: wind energy potential analysis and wind speed forecasting. In the wind energy potential analysis module, the parameters of the Weibull distribution are optimized by a multiple swarm intelligence optimization algorithm, which can provide better wind energy assessment results. In the wind speed forecasting module, the data preprocessing method can effectively eliminate the noise of the original wind speed time series, maintain the characteristics of the wind speed data, and improve the accuracy of the forecasting model. The numerical results show that the wind energy decision system not only provides an effective wind energy assessment, but can also satisfactorily approximate the actual wind speed forecasting. Therefore, it can serve as an effective tool for wind farm management and decision-making. (C) 2018 Published by Elsevier Ltd.

关键词： Wind energy potential analysis Weibull distribution Data preprocessing swarm intelligence optimization algorithm

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Study on Discrimination Model and Application of Mine Water Inrush Source Based on swarm Intelligent Neural Network

Study on Discrimination Model and Application of Mine Water ...

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第九届世界华人地质科学研讨会

作者： XU Xing SHI Pingzhou WANG Xingzhi LI Yuanzhi ZHANG Ruilin School of Safety Engineering Henan University of Engineering School of geoscience and technology Southwest Petroleum University

Mine water inrush is one of the most threatening natural disasters in the process of mine construction and production. Once mine water inrush occurs, how to quickly determine the cause of water inrush and find out the source of water inrush is the key to solve and further prevent the water inrush disaster. Because hydrochemical data is the most essential feature of groundwater, it is fast, accurate and economical to discriminate the source of mine water inrush from water quality data. Starting from the frontier disciplines of artificial intelligence, bionics, chaos theory, mathematical statistics and computer programming language, BP neural network with nonlinear mapping function was introduced into the discrimination of groundwater hydration characteristic components. At the same time, particle swarm optimization（PSO） intelligent optimization algorithm was used to globally optimize the initial weight and threshold of neural network, it could improve the convergence speed, avoid over-fitting and improve the generalization of neural network in the training process of neural network. In order to overcome the "premature" convergence of PSO, an improved particle swarm optimization algorithm（MPSO） was proposed by improving the parameters of inertia weight, cognitive coefficient and social coefficient, random mutation operator and so on. Adaptive chaotic particle swarm optimization（ACPSO） was proposed by introducing the "premature" judgment mechanism and chaotic mapping principle into PSO. MATLAB software was used to design and compile the program, four kinds of nonlinear water inrush source discrimination models, BP, PSO-BP, MPSO-BP and ACSO-BP, were constructed. The application results show that the neural network based on swarm intelligence optimization improves the discrimination accuracy of mine water inrush source, but compared with each other, ACPSO-BP is better than MPSO-B, MPSO-BP is better than PSO-BP, PSO-B is better than BP in convergence speed, accuracy and g

关键词： mine water inrush source hydration feature extraction BP neural network swarm intelligence optimization algorithm nonlinear discrimination

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The Variation Step Adaptive Glowworm swarm optimization algorithm in Optimum Log Interpretation for Reservoir with Complicated Lithology 12

The Variation Step Adaptive Glowworm Swarm Optimization Algo...

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12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Mo, Xiuwen Li, Xiao Zhang, Qiang Jilin Univ Coll Geoexplorat Sci & Technol Changchun Jilin Peoples R China

ISBN: (纸本)9781509040933

Inversion of geophysical logging data is one of the most important tasks in oil and gas exploration. Ambiguity is usually inherent for the solutions, especially for formation with complex lithology. The optimum log interpretation technique can effectively reduce the ambiguity of the interpretation results. Therefore, the Glowworm swarm optimization (GSO), one of the swarm intelligence optimization algorithms, is introduced into the log interpretation to obtain the optimal solution by virtue of its strong ability both in local and global optimization. Moreover, in order to solve the problem of slow convergence speed in the later iteration process, adaptive step is integrated into glowworm swarm optimization to form the Variation Step Adaptive Glowworm swarm optimization (VSAGSO) algorithm, which improves the accuracy and efficiency of optimizing. VSAGSO algorithm is applied for test in the tuffaceous sandstone reservoir in a certain oilfield. Comprehensively considering all kinds of errors and constraints, it could directly working-out the optimized results of reservoir parameters such as tuff content, shale content, skeleton mineral content and porosity in well accordance with the core data.

关键词： optimum well logging interpretation glowworm swarm optimization algorithm swarm intelligence optimization algorithm self-adaption tuffaceous siltstone reservoir

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