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Fault Self-Healing Cooperative Strategy of New Energy Distribution Network Based on Improved Ant Colony-Genetic Hybrid Algorithm 认领 引用 被引量：1
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作者 Fengchao Chen Aoqi Mei +2 位作者 Zheng Liu Ruhao Wu Qiwei Li 《Energy Engineering》 EI 2026年第4期247-267,共21页
With the high proportion of new energy access,the traditional fault self-healing mechanism of the distribution network is challenged.Aiming at the demand for fast recovery of new distribution network faults,this paper... With the high proportion of new energy access,the traditional fault self-healing mechanism of the distribution network is challenged.Aiming at the demand for fast recovery of new distribution network faults,this paper proposes a fault self-healing cooperative strategy for the new energy distribution network based on an improved ant colony-genetic hybrid algorithm.Firstly,the graph theory adjacency matrix is used to characterize the topology of the distribution network,and the dynamic positioning of new energy nodes is realized.Secondly,based on the output model and load characteristic model of wind,photovoltaic,and energy storage,a two-layer cooperative self-healing model of the distribution network is constructed.The upper layer is based on the improved depth-breadth hybrid search(DFS-BFS)to divide the island,with the maximum weight load recovery and the minimum number of switching actions as the goal,combined with the load priority to dynamically restore the key load.The lower layer uses the improved ant colony-genetic hybrid algorithm to solve the fault recovery path with the minimum total power loss load and the minimum network loss as the goal,generate the optimal switching sequence,and verify the power flow constraints.Finally,the simulation results based on the IEEE 33-bus system show that the proposed method can guarantee the power supply of key loads in the distribution network with high-tech energy penetration,restore the power supply of more load nodes with the least switching operation,and effectively reduce the line loss,which verifies the effectiveness and superiority of the method. 展开更多
关键词 Fault recovery identification of topology improved ant colony-genetic hybrid algorithm distribution network self-healing
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A Novel Hybrid Sine Cosine-Flower Pollination Algorithm for Optimized Feature Selection 认领 引用
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作者 Sumbul Azeem Shazia Javed +3 位作者 Farheen Ibraheem Uzma Bashir Nazar Waheed Khursheed Aurangzeb 《Computers, Materials & Continua》 SCIE EI 2026年第5期1916-1930,共15页
Data serves as the foundation for training and testing machine learning and artificial intelligencemodels.The most fundamental part of data is its attributes or features.The feature set size changes from one dataset t... Data serves as the foundation for training and testing machine learning and artificial intelligencemodels.The most fundamental part of data is its attributes or features.The feature set size changes from one dataset to another.Only the relevant features contributemeaningfully to classificationaccuracy.The presence of irrelevant features reduces the system’s effectiveness.Classification performance often deteriorates on high-dimensional datasets due to the large search space.Thus,one of the significant obstacles affecting the performance of the learning process in the majority of machine learning and data mining techniques is the dimensionality of the datasets.Feature selection(FS)is an effective preprocessing step in classification tasks.The aim of applying FS is to exclude redundant and unrelated features while retaining the most informative ones to optimize classification capability and compress computational complexity.In this paper,a novel hybrid binary metaheuristic algorithm,termed hSC-FPA,is proposed by hybridizing the Flower Pollination Algorithm(FPA)and the Sine Cosine Algorithm(SCA).Hybridization controls the exploration capacity of SCA and the exploitation behavior of FPA to maintain a balanced search process.SCA guides the global search in the early iterations,while FPA’s local pollination refines promising solutions in later stages.A binary conversion mechanism using a threshold function is implemented to handle the discrete nature of the feature selection problem.The functionality of the proposed hSC-FPA is authenticated on fourteen standard datasets from the UCI repository using the K-Nearest Neighbors(K-NN)classifier.Experimental results are benchmarked against the standalone SCA and FPA algorithms.The hSC-FPA consistently achieves higher classification accuracy,selects a more compact feature subset,and demonstrates superior convergence behavior.These findings support the stability and outperformance of the hybrid feature selection method presented. 展开更多
关键词 Classification algorithms feature selection process flower pollination algorithm hybrid model metaheuristics multi-objective optimization search algorithm sine cosine algorithm
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A Robust Damage Identification Method Based on Modified Holistic Swarm Optimization Algorithm and Hybrid Objective Function 认领 引用
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作者 Xiansong Xie Xiaoqian Qian 《Structural Durability & Health Monitoring》 EI 2026年第2期235-259,共25页
Correlation function of acceleration responses-based damage identificationmethods has been developed and employed,while they still face the difficulty in identifying local orminor structural damages.To deal with this ... Correlation function of acceleration responses-based damage identificationmethods has been developed and employed,while they still face the difficulty in identifying local orminor structural damages.To deal with this issue,a robust structural damage identification method is developed,integrating a modified holistic swarm optimization(MHSO)algorithm with a hybrid objective function.The MHSO is developed by combining Hammersley sequencebased population initialization,chaotic search around the worst solution,and Hooke-Jeeves pattern search around the best solution,thereby improving both global exploration and local exploitation capabilities.A hybrid objective function is constructed by merging acceleration correlation function-based and strain correlation function-based objective functions,effectively leveraging the complementary sensitivities of global and local responses.To further suppress spurious solutions and promote sparsity in parameter estimation,an additional L0.5 regularization term is introduced.The effectiveness of the proposed method is validated through numerical simulations on a simply supported beam and a steel girder benchmark structure.Comparative studies with sequential quadratic programming,genetic algorithm,andHSO demonstrate that theMHSOachieves superior accuracy and convergence efficiency,even with limited sensors and 20%noise-contaminated measurements.Results highlight that the hybrid objective function significantly enhances the detection of both major and minor damages,while the inclusion of sparse regularization improves robustness against noise and model uncertainties.The findings indicate that the proposed framework provides a reliable and computationally efficient solution for simultaneous localization and quantification of structural damages,offering promising applicability to real-world structural health monitoring scenarios. 展开更多
关键词 Damage identification holistic swarm optimization algorithm combined correlation function hybrid objective function sparse regularization grid structure
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Hybrid adaptive machine learning approach for detection and mitigation of GNSS spoofing through enhanced osprey optimization algorithm 认领 引用
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作者 KOTI Sushmitha SANDHYA Rachamalla 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第3期1059-1080,共22页
Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,veloci... Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,velocity,and time(PVT).Several GNSS devices are exposed to detect spoofing attacks due to the use of unsafe locations.In addition,misleading signals are intentionally used to generate timing and position,and GNSS signal spoofing provides a constant risk to consumers.In past works,the implementation of the Global Positioning System(GPS)in autonomous vehicle navigation might be endangered by spoofing.To mitigate these issues,this task develops a hybrid machine-learning method for mitigating and detecting GNSS spoofing attacks.The developed model is processed with three phases:data collection,feature extraction,and detection.Initially,the required data is taken from the standard resource.Then,the data is given to the feature extraction phase.The features of the data are retrieved using the principal component analysis(PCA)and t-distributed stochastic neighbor embedding(t-SNE)model.The features obtained from the collected data are transferred to the detection phase.In the final phase,the GNSS spoofing detection and mitigation is executed using a machine learning method called as hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP).Enhanced osprey optimization algorithm(EOOA)is utilized for optimizing the variables to enhance the efficacy of models and achieves greater performance than other standard models. 展开更多
关键词 Global Navigation Satellite System(GNSS) detection and mitigation of GNSS t-distributed stochastic neighbor embedding(t-SNE) enhanced osprey optimization algorithm principal component analysis(PCA) hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP)
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SL-COA:Hybrid Efficient and Enhanced Coati Optimization Algorithm for Structural Reliability Analysis 认领 引用
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作者 Yunhan Ling Huajun Peng +4 位作者 Yiqing Shi Chao Xu Jingzhen Yan Jingjing Wang Hui Ma 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第4期767-808,共42页
Thetraditional first-order reliability method(FORM)often encounters challengeswith non-convergence of results or excessive calculation when analyzing complex engineering problems.To improve the global convergence spee... Thetraditional first-order reliability method(FORM)often encounters challengeswith non-convergence of results or excessive calculation when analyzing complex engineering problems.To improve the global convergence speed of structural reliability analysis,an improved coati optimization algorithm(COA)is proposed in this paper.In this study,the social learning strategy is used to improve the coati optimization algorithm(SL-COA),which improves the convergence speed and robustness of the newheuristic optimization algorithm.Then,the SL-COAis comparedwith the latest heuristic optimization algorithms such as the original COA,whale optimization algorithm(WOA),and osprey optimization algorithm(OOA)in the CEC2005 and CEC2017 test function sets and two engineering optimization design examples.The optimization results show that the proposed SL-COA algorithm has a high competitiveness.Secondly,this study introduces the SL-COA algorithm into the MPP(Most Probable Point)search process based on FORM and constructs a new reliability analysis method.Finally,the proposed reliability analysis method is verified by four mathematical examples and two engineering examples.The results show that the proposed SL-COA-assisted FORM exhibits fast convergence and avoids premature convergence to local optima as demonstrated by its successful application to problems such as composite cylinder design and support bracket analysis. 展开更多
关键词 Hybrid reliability analysis single-loop interactive hybrid analysis most probability point metaheuristic algorithms coati optimization algorithm
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Dynamic Boundary Optimization via IDBO-VMD:A Novel Power Allocation Strategy for Hybrid Energy Storage with Enhanced Grid Stability 认领 引用 被引量：1
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作者 Zujun Ding Qi Xiang +10 位作者 Chengyi Li Mengyu Ma Chutong Zhang Xinfa Gu Jiaming Shi Hui Huang Aoyun Xia Wenjie Wang Wan Chen Ziluo Yu Jie Ji 《Energy Engineering》 EI 2026年第1期527-552,共26页
In order to address environmental pollution and resource depletion caused by traditional power generation,this paper proposes an adaptive iterative dynamic-balance optimization algorithm that integrates the Improved D... In order to address environmental pollution and resource depletion caused by traditional power generation,this paper proposes an adaptive iterative dynamic-balance optimization algorithm that integrates the Improved Dung Beetle Optimizer(IDBO)with VariationalMode Decomposition(VMD).The IDBO-VMD method is designed to enhance the accuracy and efficiency of wind-speed time-series decomposition and to effectively smooth photovoltaic power fluctuations.This study innovatively improves the traditional variational mode decomposition(VMD)algorithm,and significantly improves the accuracy and adaptive ability of signal decomposition by IDBO selfoptimization of key parameters K and a.On this basis,Fourier transform technology is used to define the boundary point between high frequency and low frequency signals,and a targeted energy distribution strategy is proposed:high frequency fluctuations are allocated to supercapacitors to quickly respond to transient power fluctuations;Lowfrequency components are distributed to lead-carbon batteries,optimizing long-term energy storage and scheduling efficiency.This strategy effectively improves the response speed and stability of the energy storage system.The experimental results demonstrate that the IDBO-VMD algorithm markedly outperforms traditional methods in both decomposition accuracy and computational efficiency.Specifically,it effectively reduces the charge–discharge frequency of the battery,prolongs battery life,and optimizes the operating ranges of the state-of-charge(SOC)for both leadcarbon batteries and supercapacitors.In addition,the energy management strategy based on the algorithm not only improves the overall energy utilization efficiency of the system,but also shows excellent performance in the dynamic management and intelligent scheduling of renewable energy generation. 展开更多
关键词 Energy efficiency hybrid energy storage system intelligent algorithm power fluctuation mitigation renewable energy
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Derivative Free and Dispatch Algorithm-Based Optimization and Power System Assessment of a Biomass-PV-Hydrogen Storage-Grid Hybrid Renewable Microgrid for Agricultural Applications 认领 引用
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作者 Md.Fatin Ishraque Akhlaqur Rahman +5 位作者 Kamil Ahmad Sk.A.Shezan Md.Meheraf Hossain Sheikh Rashel Al Ahmed Md.Iasir Arafat Noor E Nahid Bintu 《Energy Engineering》 EI 2025年第8期3347-3375,共29页
In this research work,the localized generation from renewable resources and the distribution of energy to agricultural loads,which is a local microgrid concept,have been considered,and its feasibility has been assesse... In this research work,the localized generation from renewable resources and the distribution of energy to agricultural loads,which is a local microgrid concept,have been considered,and its feasibility has been assessed.Two dispatch algorithms,named Cycle Charging and Load Following,are implemented to find the optimal solution(i.e.,net cost,operation cost,carbon emission.energy cost,component sizing,etc.)of the hybrid system.The microgrid is also modeled in the DIgSILENT Power Factory platform,and the respective power system responses are then evaluated.The development of dispatch algorithms specifically tailored for agricultural applications has enabled to dynamically manage energy flows,responding to fluctuating demands and resource availability in real-time.Through careful consideration of factors such as seasonal variations and irrigation requirements,these algorithms have enhanced the resilience and adaptability of the microgrid to dynamic operational conditions.However,it is revealed that both approaches have produced the same techno-economic results showing no significant difference.This illustrates the fact that the considered microgrid can be implemented with either strategy without significant fluctuation in performance.The study has shown that the harmful gas emission has also been limited to only 17,928 kg/year of CO2,and 77.7 kg/year of Sulfur Dioxide.For the proposed microgrid and load profile of 165.29 kWh/day,the net present cost is USD 718,279,and the cost of energy is USD 0.0463 with a renewable fraction of 97.6%.The optimal sizes for PV,Bio,Grid,Electrolyzer,and Converter are 1494,500,999,999,500,and 495 kW,respectively.For a hydrogen tank(HTank),the optimal size is found to be 350 kg.This research work provides critical insights into the techno-economic feasibility and environmental impact of integrating biomass-PV-hydrogen storage-Grid hybrid renewable microgrids into agricultural settings. 展开更多
关键词 Renewable energy derivative-free algorithm optimization hybrid system energy storage
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Hybrid genetic simulated annealing algorithm for agile Earth observation satellite scheduling considering cloud cover distribution 认领 引用
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作者 SUN Haiquan WANG Zhilong +1 位作者 HU Xiaoxuan XIA Wei 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第6期1595-1612,共18页
Agile earth observation satellites(AEOSs)represent a new generation of satellites with three degrees of freedom(pitch,roll,and yaw);they possess a long visible time window(VTW)for ground targets and support imaging at... Agile earth observation satellites(AEOSs)represent a new generation of satellites with three degrees of freedom(pitch,roll,and yaw);they possess a long visible time window(VTW)for ground targets and support imaging at any moment within the VTW.However,different observation times demonstrate different cloud cover distributions,which exhibit different effects on the AEOS observation.Previous studies ignored pitch angles,discretized VTWs,or fixed cloud cover for every VTW,which led to the loss of intermediate observation states,thus these studies are not suitable for AEOS scheduling considering cloud cover distribution.In this study,a relationship formula between the cloud cover and observation time is proposed to calculate the cloud cover for every observation time,and a relationship formula between the observation time and pitch angle is designed to calculate the pitch angle for every observation time in the VTW.A refined model including the pitch angle,roll angle,and cloud cover distribution is established,which can make the scheme closer to the actual application of AEOSs.A hybrid genetic simulated annealing(HGSA)algorithm for AEOS scheduling is proposed,which integrates the advantages of genetic and simulated annealing algorithms and can effectively avoid falling into a local optimal solution.The experiments are conducted to compare the proposed algorithm with the traditional algorithms,the results verify that the proposed model and algorithm are efficient and effective for AEOS scheduling considering cloud cover distribution. 展开更多
关键词 agile Earth observation satellite cloud cover distribution hybrid genetic simulated annealing algorithm
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Hybrid genetic algorithm for parametric optimization of surface pipeline networks in underground natural gas storage harmonized injection and production conditions 认领 引用
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作者 Jun Zhou Zichen Li +4 位作者 Shitao Liu Chengyu Li Yunxiang Zhao Zonghang Zhou Guangchuan Liang 《Natural Gas Industry B》 2025年第2期234-250,共17页
The surface injection and production system(SIPS)is a critical component for effective injection and production processes in underground natural gas storage.As a vital channel,the rational design of the surface inject... The surface injection and production system(SIPS)is a critical component for effective injection and production processes in underground natural gas storage.As a vital channel,the rational design of the surface injection and production(SIP)pipeline significantly impacts efficiency.This paper focuses on the SIP pipeline and aims to minimize the investment costs of surface projects.An optimization model under harmonized injection and production conditions was constructed to transform the optimization problem of the SIP pipeline design parameters into a detailed analysis of the injection condition model and the production condition model.This paper proposes a hybrid genetic algorithm generalized reduced gradient(HGA-GRG)method,and compares it with the traditional genetic algorithm(GA)in a practical case study.The HGA-GRG demonstrated significant advantages in optimization outcomes,reducing the initial cost by 345.371×104 CNY compared to the GA,validating the effectiveness of the model.By adjusting algorithm parameters,the optimal iterative results of the HGA-GRG were obtained,providing new research insights for the optimal design of a SIPS. 展开更多
关键词 Underground natural gas storage Surface injection and production pipeline Parameter optimization Hybrid genetic algorithm
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A Bi-Level Optimization Model and Hybrid Evolutionary Algorithm for Wind Farm Layout with Different Turbine Types 认领 引用
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作者 Erping Song Zipin Yao 《Energy Engineering》 EI 2025年第12期5129-5147,共19页
Wind farm layout optimization is a critical challenge in renewable energy development,especially in regions with complex terrain.Micro-siting of wind turbines has a significant impact on the overall efficiency and eco... Wind farm layout optimization is a critical challenge in renewable energy development,especially in regions with complex terrain.Micro-siting of wind turbines has a significant impact on the overall efficiency and economic viability of wind farm,where the wake effect,wind speed,types of wind turbines,etc.,have an impact on the output power of the wind farm.To solve the optimization problem of wind farm layout under complex terrain conditions,this paper proposes wind turbine layout optimization using different types of wind turbines,the aim is to reduce the influence of the wake effect and maximize economic benefits.The linear wake model is used for wake flow calculation over complex terrain.Minimizing the unit energy cost is taken as the objective function,considering that the objective function is affected by cost and output power,which influence each other.The cost function includes construction cost,installation cost,maintenance cost,etc.Therefore,a bi-level constrained optimization model is established,in which the upper-level objective function is to minimize the unit energy cost,and the lower-level objective function is to maximize the output power.Then,a hybrid evolutionary algorithm is designed according to the characteristics of the decision variables.The improved genetic algorithm and differential evolution are used to optimize the upper-level and lower-level objective functions,respectively,these evolutionary operations search for the optimal solution as much as possible.Finally,taking the roughness of different terrain,wind farms of different scales and different types of wind turbines as research scenarios,the optimal deployment is solved by using the algorithm in this paper,and four algorithms are compared to verify the effectiveness of the proposed algorithm. 展开更多
关键词 Bi-level optimization genetic algorithm differential evolution hybrid evolutionary algorithm wind farm layout
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Federated Multi-Label Feature Selection via Dual-Layer Hybrid Breeding Cooperative Particle Swarm Optimization with Manifold and Sparsity Regularization 认领 引用
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作者 Songsong Zhang Huazhong Jin +5 位作者 Zhiwei Ye Jia Yang Jixin Zhang Dongfang Wu Xiao Zheng Dingfeng Song 《Computers, Materials & Continua》 SCIE EI 2026年第1期1141-1159,共19页
Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant chal... Multi-label feature selection(MFS)is a crucial dimensionality reduction technique aimed at identifying informative features associated with multiple labels.However,traditional centralized methods face significant challenges in privacy-sensitive and distributed settings,often neglecting label dependencies and suffering from low computational efficiency.To address these issues,we introduce a novel framework,Fed-MFSDHBCPSO—federated MFS via dual-layer hybrid breeding cooperative particle swarm optimization algorithm with manifold and sparsity regularization(DHBCPSO-MSR).Leveraging the federated learning paradigm,Fed-MFSDHBCPSO allows clients to perform local feature selection(FS)using DHBCPSO-MSR.Locally selected feature subsets are encrypted with differential privacy(DP)and transmitted to a central server,where they are securely aggregated and refined through secure multi-party computation(SMPC)until global convergence is achieved.Within each client,DHBCPSO-MSR employs a dual-layer FS strategy.The inner layer constructs sample and label similarity graphs,generates Laplacian matrices to capture the manifold structure between samples and labels,and applies L2,1-norm regularization to sparsify the feature subset,yielding an optimized feature weight matrix.The outer layer uses a hybrid breeding cooperative particle swarm optimization algorithm to further refine the feature weight matrix and identify the optimal feature subset.The updated weight matrix is then fed back to the inner layer for further optimization.Comprehensive experiments on multiple real-world multi-label datasets demonstrate that Fed-MFSDHBCPSO consistently outperforms both centralized and federated baseline methods across several key evaluation metrics. 展开更多
关键词 Multi-label feature selection federated learning manifold regularization sparse constraints hybrid breeding optimization algorithm particle swarm optimizatio algorithm privacy protection
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Coordinated Optimization Strategy for Hybrid Energy Storage toward High-PV-Penetration Distribution Networks 认领 引用
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作者 Yechun Xin Xinxin Cheng +3 位作者 Yanxu Wang Weiru Wang Jiexiang Han Zhen Niu 《Energy Engineering》 EI 2026年第5期76-96,共21页
Addressing voltage violations and renewable energy absorption bottlenecks arising from high-penetration photovoltaic(PV)integration,this paper proposes a hierarchical optimisation architecture for a Hybrid Energy Stor... Addressing voltage violations and renewable energy absorption bottlenecks arising from high-penetration photovoltaic(PV)integration,this paper proposes a hierarchical optimisation architecture for a Hybrid Energy Storage System(HESS)based on microgrid-distribution network coordination to enhance collaborative regulation of energy storage across multiple microgrids.The methodology comprises a PV hosting capacity assessment model and an HESS operation model that accounts for power supply reliability,forming a two-layer planning framework that integrates distributed decision-making with centralized coordination.At the microgrid level,HESS capacity is determined to minimise the local comprehensive cost,while the allocation ratio between lithium-based and hydrogen-based energy storage is optimised to maximise renewable energy utilization.At the distribution network level,a global collaborative dispatch of multi-microgrid energy storage is performed to minimise total system cost and voltage deviation,thereby achieving cross-regional power balance and voltage support.Simulation verification is conducted using actual distribution network data from Zhejiang Province and a 33-node test system.The results indicate that the proposed coordinated operation strategy can effectively improve voltage quality,enhance the PV absorption rate,and increase economic benefits. 展开更多
关键词 Hybrid energy storage capacity configuration mayfly algorithm high proportion of photovoltaics bi-level optimization
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Hybrid Flow Shop Rescheduling Approach Based on Hybrid-Driven Mechanism and Improved Multi-Objective WOA 认领 引用
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作者 Feng Lv Xin Xu +1 位作者 Cheng Yang Yixuan Tang 《Computers, Materials & Continua》 SCIE EI 2026年第7期1982-2009,共28页
To ensure an effective disturbance response and maintain continuous production in hybrid flow shops,this paper focuses on the design of a rescheduling method.A rescheduling model is constructed that minimizes the make... To ensure an effective disturbance response and maintain continuous production in hybrid flow shops,this paper focuses on the design of a rescheduling method.A rescheduling model is constructed that minimizes the makespan,total tardiness,and scheme deviation degree.A hybrid rescheduling driving mechanism based on the latest completion time is designed to effectively trigger rescheduling.The Whale Optimization Algorithm(WOA)is improved by integrating the good point set theory,nonlinear control parameter strategy,and Differential Evolution(DE)algorithm.Moreover,non-dominated sorting and a dynamic external archive mechanism based on crowding distance are introduced to make it suitable for multi-objective optimization problems.The superiority of the Improved Multi-objective Whale Optimization Algorithm(IMOWOA)and the effectiveness of the improved mechanisms are verified through comparative experiments and ablation experiments.Taking the final assembly production line of an agricultural machinery equipment enterprise as an example,a rescheduling scheme is generated based on the practical production requirements,which verifies the feasibility and effectiveness of the proposed method. 展开更多
关键词 Hybrid flow shop production disturbance production rescheduling rescheduling driving mechanism improved multi-objective whale optimization algorithm
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Distributed Iterative Learning Control for Load Balancing in Flexible AC/DC Hybrid Distribution Systems 认领 引用
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作者 Hong Zhang Bin Xu +4 位作者 Jinzhong Li Xiaoxiao Meng Cheng Qian Wei Ma Yuguang Xie 《Energy Engineering》 EI 2026年第5期298-316,共19页
The increasing integration of distributed renewable energy sources in the distribution network leads to unbalanced load rates in the distribution network.The traditional load balancing methods are mainly based on netw... The increasing integration of distributed renewable energy sources in the distribution network leads to unbalanced load rates in the distribution network.The traditional load balancing methods are mainly based on network reconfiguration,which have problems such as a long time scale and poor adaptability.In response to these issues,this paper proposes a distributed iterative learning control(ILC)strategy for load balancing in flexible AC/DC hybrid distribution systems.This method combines the consensus algorithm with the ILC mechanism to construct a multi-terminal AC/DC flexible interconnection system model.It is only necessary to measure the load rate of adjacent units without observing the overall system status,which greatly reduces complexity and enhances robustness.In this paper,a new energy photovoltaic and energy storage integrated system was built through MATLAB/Simulink simulation,and the effectiveness of the proposed strategy under normal working conditions and port faults was verified through this system.Through comparative studies with event-triggered control and traditional consensus algorithms,as well as real-time simulations on the RT-LAB simulation platform,it has been confirmed that this method has superior performance in terms of convergence speed,steady-state accuracy,and dynamic response,and has the potential to be applied in practical models.It is suitable for application in medium and low voltage distribution systems with new energy access. 展开更多
关键词 AC/DC hybrid distribution systems iterative learning algorithm load rate
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An Architecture-Aware Hybrid CPU-GPU Approach for WEMA-Based Fast Pattern Matching in Network Intrusion Detection Systems 认领 引用
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作者 Adnan Hnaif Hanadi Al-Shawabkah +1 位作者 Ayman Alqafaan Mohammad Alia 《Computers, Materials & Continua》 SCIE EI 2026年第9期1100-1115,共16页
Many fast pattern-matching mechanisms are used in NIDS(Network Intrusion Detection Systems)to filter higher volumes of network traffic prior to invoking expensive rule verification stages.This filtering phase in signa... Many fast pattern-matching mechanisms are used in NIDS(Network Intrusion Detection Systems)to filter higher volumes of network traffic prior to invoking expensive rule verification stages.This filtering phase in signature-based engines,such as Snort,needs to preserve exact matching semantics while being able to process at high throughput on commodity hardware.Here,we introduce a hybrid CPU–GPU architecture-aware framework for exact multi-pattern matching based on the Weighted Exact Matching Algorithm(WEMA).WEMA performs the most relevant matching based on deterministic ordered indexing of category units,which eliminates chaotic control flow(which occurs with automata learning)and also brings out more regular memory access.An extensive evaluation across CPU-only,GPU-only,and hybrid CPU–GPU execution models is performed to analyze how WEMA interacts with heterogeneous hardware architectures.Informed by these observations,we propose a hybrid design with CPU-based control-intensive tasks—rule parsing,index construction,batching,and rule verification—retained on the CPU while selective data-parallel payload scanning is off-loaded to the GPU.Experimental results show that CPU-only execution on a commodity multicore CPU and integrated GPU platform delivers stable performance across the entire range of payload sizes,while the hybrid model achieves modest but repeatable improvements for small and medium workloads.The architecture evaluated here offers a relatively small advantage for GPU-only execution.Given the very nature of NIDS alongside the results provided in this paper,it is clear that WEMA-based acceleration on NIDS highly depends on hardware features and workload size,while selective,architecture-aware GPU utilization is crucial to maintain deterministic run-time characteristics in security-critical applications. 展开更多
关键词 NIDS WEMA exact string-matching algorithms hybrid CPU–GPU architecture heterogeneous computing
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A Hybrid MZOA-PSO Optimized Cascaded PI(1+DD)-PI-PID Controller for Frequency Stability of Interconnected Power Systems with Renewable Energy and Electric Vehicles 认领 引用
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作者 AL-Wesabi Ibrahim Hassan M.Hussein Farh +3 位作者 Jiazhu Xu Mohamad A.Alawad Ahmed Alqurashi Abdullrahman A.Al-Shamma’a 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第7期650-697,共48页
Load frequency control(LFC)in interconnected power systems has always been a challenging task in the presence of uncertainty and variability in the power systems arising primarily due to the integration of renewable e... Load frequency control(LFC)in interconnected power systems has always been a challenging task in the presence of uncertainty and variability in the power systems arising primarily due to the integration of renewable energy sources and the impact of electric vehicles on the power system.Although various PI/PID and other advanced control strategies have been employed for LFC in power systems,the existing methods have shown some limitations in terms of dynamic flexibility and robustness in the presence of nonlinearities and couplings in the power systems.Moreover,the optimization methods employed for the tuning of the controllers have shown some limitations in terms of the balance between global and local search abilities of the optimization functions.To overcome the limitations of the existing methods and optimization functions,a hybrid Modified Zebra Optimization Algorithm-Particle Swarm Optimization(MZOA-PSO)is presented in this paper for the optimization of a cascaded PI(1+DD)-PI-PID controller for LFC in power systems.The MZOA enhances the original ZOA by chaotic initialization,adaptive parameter control,and Lévy-flight foraging to improve the global search ability,while PSO ensures efficient local search ability.The optimizer is first validated using four benchmark functions,achieving the global optimum for the Booth and Zakharov functions,a mean value of 2.13×10−28 with a 98%success rate for Rosenbrock,and 3.21×10−81 for Schwefel 2.22.Under a 1%step load perturbation,the proposed controller achieves a 13 s settling time,zero negative deviation in Area 2,a maximum positive excursion of 0.10 Hz,and tie-line undershoot limited to−0.10 p.u.Under random load variations,deviations remain within±0.03 Hz and±0.02 p.u.Under RES and EV integration,the peak frequency deviation is reduced to 0.46 Hz in Area 1.These results confirm that the proposed hybrid MZOA-PSO tuned cascaded controller provides improved damping,faster stabilization,and stronger robustness for modern interconnected LFC systems. 展开更多
关键词 Load frequency control cascaded PI(1+DD)-PI-PID controller hybrid MZOA-PSO algorithm interconnected power systems performance optimization
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Multi-Level Subpopulation-Based Particle Swarm Optimization Algorithm for Hybrid Flow Shop Scheduling Problem with Limited Buffers 认领 引用 被引量：1
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作者 Yuan Zou Chao Lu +1 位作者 Lvjiang Yin Xiaoyu Wen 《Computers, Materials & Continua》 SCIE EI 2025年第8期2305-2330,共26页
The shop scheduling problem with limited buffers has broad applications in real-world production scenarios,so this research direction is of great practical significance.However,there is currently little research on th... The shop scheduling problem with limited buffers has broad applications in real-world production scenarios,so this research direction is of great practical significance.However,there is currently little research on the hybrid flow shop scheduling problem with limited buffers(LBHFSP).This paper deeply investigates the LBHFSP to optimize the goal of the total completion time.To better solve the LBHFSP,a multi-level subpopulation-based particle swarm optimization algorithm(MLPSO)is proposed,which is founded on the attributes of the LBHFSP and the shortcomings of the basic PSO(particle swarm optimization)algorithm.In MLPSO,firstly,considering the impact of the limited buffers on the process of subsequent operations,a specific circular decoding strategy is developed to accommodate the characteristics of limited buffers.Secondly,an initialization strategy based on blocking time is designed to enhance the quality and diversity of the initial population.Afterward,a multi-level subpopulation collaborative search is developed to prevent being trapped in a local optimum and improve the global exploration capability.Additionally,a local search strategy based on the first blocked job is designed to enhance the MLPSO algorithm’s exploitation capability.Lastly,numerous experiments are carried out to test the performance of the proposed MLPSO by comparing it with classical intelligent optimization and popular algorithms in recent years.The results confirm that the proposed MLPSO has an outstanding performance when compared to other algorithms when solving LBHFSP. 展开更多
关键词 Hybrid flow shop scheduling problem limited buffers PSO algorithm collaborative search blocking phenomenon
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Multi-Objective Hybrid Sailfish Optimization Algorithm for Planetary Gearbox and Mechanical Engineering Design Optimization Problems 认领 引用 被引量：1
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作者 Miloš Sedak Maja Rosic Božidar Rosic 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第2期2111-2145,共35页
This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Op... This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain. 展开更多
关键词 Multi-objective optimization planetary gearbox gear efficiency sailfish optimization differential evolution hybrid algorithms
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Grid-Connected/Islanded Switching Control Strategy for Photovoltaic Storage Hybrid Inverters Based on Modified Chimpanzee Optimization Algorithm 认领 引用
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作者 Chao Zhou Narisu Wang +1 位作者 Fuyin Ni Wenchao Zhang 《Energy Engineering》 EI 2025年第1期265-284,共20页
Uneven power distribution,transient voltage,and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island modes.In response to these issues,th... Uneven power distribution,transient voltage,and frequency deviations are observed in the photovoltaic storage hybrid inverter during the switching between grid-connected and island modes.In response to these issues,this paper proposes a grid-connected/island switching control strategy for photovoltaic storage hybrid inverters based on the modified chimpanzee optimization algorithm.The proposed strategy incorporates coupling compensation and power differentiation elements based on the traditional droop control.Then,it combines the angular frequency and voltage amplitude adjustments provided by the phase-locked loop-free pre-synchronization control strategy.Precise pre-synchronization is achieved by regulating the virtual current to zero and aligning the photovoltaic storage hybrid inverter with the grid voltage.Additionally,two novel operators,learning and emotional behaviors are introduced to enhance the optimization precision of the chimpanzee algorithm.These operators ensure high-precision and high-reliability optimization of the droop control parameters for photovoltaic storage hybrid inverters.A Simulink model was constructed for simulation analysis,which validated the optimized control strategy’s ability to evenly distribute power under load transients.This strategy effectively mitigated transient voltage and current surges during mode transitions.Consequently,seamless and efficient switching between gridconnected and island modes was achieved for the photovoltaic storage hybrid inverter.The enhanced energy utilization efficiency,in turn,offers robust technical support for grid stability. 展开更多
关键词 Photovoltaic storage hybrid inverters modified chimpanzee optimization algorithm droop control seamless switching
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Hybrid Beamforming for MU-MISO Communication via Deep Unfolding 认领 引用
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作者 Liu Dangpeng He Xin He Haoming 《China Communications》 SCIE EI CSCD 2026年第2期260-267,共8页
In hybrid beamforming design using the conventional gradient projection(GP)algorithm,it is common to use a fixed step size,which results in a slow convergence rate and unsatisfactory achievable rate performance.This p... In hybrid beamforming design using the conventional gradient projection(GP)algorithm,it is common to use a fixed step size,which results in a slow convergence rate and unsatisfactory achievable rate performance.This paper employs a deep unfolding algorithm within a small fixed number of iterations to tackle the hybrid beamforming optimization problem.The optimal step size is obtained by combining the conventional GP algorithm with the deep learning technique,and every step in deep learning is explainable.Simulation results show that the proposed deep unfolding algorithm demonstrates a lower computational time and superior achievable rate performance than the conventional GP algorithm. 展开更多
关键词 deep unfolding algorithm hybrid beamforming unit modulus constraint
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