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Clinically applicable artificial intelligence algorithm for the diagnosis,evaluation,and monitoring of acute retinal necrosis 认领 引用 被引量：2
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作者 Lei FENG Daizhan ZHOU +5 位作者 Chenqi LUO Junhui SHEN Wenzhe WANG Yifei LU Jian WU Ke YAO 《Journal of Zhejiang University-SCIENCE B》 SCIE CAS CSCD 2021年第6期504-511,共8页
The prompt detection and proper evaluation of necrotic retinal region are especially important for the diagnosis and treatment of acute retinal necrosis(ARN).The potential application of artificial intelligence(AI)alg... The prompt detection and proper evaluation of necrotic retinal region are especially important for the diagnosis and treatment of acute retinal necrosis(ARN).The potential application of artificial intelligence(AI)algorithms in these areas of clinical research has not been reported previously.The present study aims to create a computational algorithm for the automated detection and evaluation of retinal necrosis from retinal fundus photographs.A total of 149 wide-angle fundus photographs from40 eyes of 32 ARN patients were collected,and the U-Net method was used to construct the AI algorithm.Thereby,a novel algorithm based on deep machine learning in detection and evaluation of retinal necrosis was constructed for the first time.This algorithm had an area under the receiver operating curve of 0.92,with 86%sensitivity and 88%specificity in the detection of retinal necrosis.For the purpose of retinal necrosis evaluation,necrotic areas calculated by the AI algorithm were significantly positively correlated with viral load in aqueous humor samples(R2=0.7444,P<0.0001)and therapeutic response of ARN(R2=0.999,P<0.0001).Therefore,our AI algorithm has a potential application in the clinical aided diagnosis of ARN,evaluation of ARN severity,and treatment response monitoring. 展开更多
关键词 Acute retinal necrosis(ARN) Artificial intelligence(AI)algorithm Clinical application
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Application of swarm intelligence algorithm on PFSP 认领 引用
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作者 LI Minnan LIU Sheng 《International English Education Research》 2017年第2期52-55,共4页
With today＇s global economic downturn and the increasingly fierce market competition, manufacturing enterprises must guarantee the efficient operation of production system, in order to get ahead in the competition, s... With today＇s global economic downturn and the increasingly fierce market competition, manufacturing enterprises must guarantee the efficient operation of production system, in order to get ahead in the competition, scheduling reasonable flow shop production systems can improve productivity and equipment utilization rate, reduce production costs. So the production system of flow shop scheduling problem has become one of the core problems ofmanufactaring enterprises the use of more and more. 展开更多
关键词 PFSP swarm intelligence algorithm combinatorial optimiTafion industrial manufacturing
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A constraint relaxation-based intelligence algorithm for workshop layouts with automated guided vehicle systems 认领 引用
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作者 Ran Liu Chengkai Wang 《Journal of Management Science and Engineering》 CSCD 2025年第2期225-244,共20页
With the widespread adoption of automated guided vehicle(AGV)systems for material handling in manufacturing plants,it has become practical and crucial to delve into the layout problem associated with AGV systems.In th... With the widespread adoption of automated guided vehicle(AGV)systems for material handling in manufacturing plants,it has become practical and crucial to delve into the layout problem associated with AGV systems.In this work,we focus on a unique layout problem encountered in a hybrid workshop where AGV systems are employed for transporting semiproducts along the manufacturing line.Several distinctive features in this system contribute to the challenge of the problem.Notably,manufacturing occurs in an uncertain environment,and certain manufacturing cells may produce semiproducts that do not meet quality standards,necessitating repair.Additionally,each AGV requires recharging in a designated area within the workshop.Given that the proposed layout problem is NP-hard,we present an intelligence variable neighborhood search heuristic integrated with a constraint relaxation strategy to address its complexity.The numerical results demonstrate the algorithm's ability to generate high-quality solutions within a reasonable timeframe,even for large-scale test instances.The layout solutions obtained through our algorithm outperform those produced by the CPLEX solver and the practical layouts devised by the company.This highlights the efficacy of our approach in tackling the unique challenges posed by the layout problem in a hybrid workshop with an AGV system. 展开更多
关键词 Facility layout Automated guided vehicle Intelligence algorithm Constraint relaxation
Multi-objective scheduling of relief logistics based on swarm intelligence algorithms and spatio-temporal traffic flow 认领 引用 被引量：3
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作者 Zhiming Ding Zilin Zhao +1 位作者 Detian Liu Yang Cao 《Journal of Safety Science and Resilience》 EI CSCD 2021年第4期222-229,共8页
Emergency supplies scheduling needs to consider the state of the demanders,and reasonably scheduling and resource allocation are the heart of efficient rescue.Taking rescue time,scheduling cost and demanders’satisfac... Emergency supplies scheduling needs to consider the state of the demanders,and reasonably scheduling and resource allocation are the heart of efficient rescue.Taking rescue time,scheduling cost and demanders’satisfac-tion as goals,in this paper,an emergency supplies scheduling model based on multi-objective optimization was proposed to provide a wealth of decision-making information.Then four multi-objective optimization algorithms are employed to obtain the optimal set of scheduling models.In addition,we design the minimum time cost model and the shortest route cost model by considering the change of the road network status.The extensive simulation experiments are conducted on a real urban traffic dataset.The experimental results show that the two cost models can serve different scheduling needs and provide efficient scheduling for emergency supplies. 展开更多
关键词 Logistics scheduling MOP Swarm intelligence algorithms Spatio-temporal trajectory Traffic flow
A Novel Multi-Modal Neurosymbolic Reasoning Intelligent Algorithm for BLMP Equation 认领 引用 被引量：1
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作者 Hanwen Zhang Runfa Zhang Qirang Liu 《Chinese Physics Letters》 SCIE EI CAS CSCD 2025年第10期13-17,共5页
The(3+1)-dimensional Boiti-Leon-Manna-Pempinelli(BLMP)equation serves as a crucial nonlinear evolution equation in mathematical physics,capable of characterizing complex nonlinear dynamic phenomena in three-dimensiona... The(3+1)-dimensional Boiti-Leon-Manna-Pempinelli(BLMP)equation serves as a crucial nonlinear evolution equation in mathematical physics,capable of characterizing complex nonlinear dynamic phenomena in three-dimensional space and one-dimensional time.With broad applications spanning fluid dynamics,shallow water waves,plasma physics,and condensed matter physics,the investigation of its solutions holds significant importance.Traditional analytical methods face limitations due to their dependence on bilinear forms.To overcome this constraint,this letter proposes a novel multi-modal neurosymbolic reasoning intelligent algorithm(MMNRIA)that achieves 100%accurate solutions for nonlinear partial differential equations without requiring bilinear transformations.By synergistically integrating neural networks with symbolic computation,this approach establishes a new paradigm for universal analytical solutions of nonlinear partial differential equations.As a practical demonstration,we successfully derive several exact analytical solutions for the(3+1)-dimensional BLMP equation using MMNRIA.These solutions provide a powerful theoretical framework for studying intricate wave phenomena governed by nonlinearity and dispersion effects in three-dimensional physical space. 展开更多
关键词 intelligent algorithm dimensional Boiti Leon Manna Pempinelli equation fluid dynamicsshallow water wavesplasma physicsand nonlinear evolution equation condensed matter physicsthe neurosymbolic reasoning characterizing complex nonlinear dynamic phenomena analytical methods
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Optimization model for performance-based warranty decision of degraded systems based on improved sparrow search algorithm 认领 引用
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作者 DONG Enzhi CHENG Zhonghua +3 位作者 LIU Zichang ZHU Xi WANG Rongcai BAI Yongsheng 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第5期1259-1280,共22页
Performance-based warranties(PBWs)are widely used in industry and manufacturing.Given that PBW can impose financial burdens on manufacturers,rational maintenance decisions are essential for expanding profit margins.Th... Performance-based warranties(PBWs)are widely used in industry and manufacturing.Given that PBW can impose financial burdens on manufacturers,rational maintenance decisions are essential for expanding profit margins.This paper proposes an optimization model for PBW decisions for systems affected by Gamma degradation processes,incorporating periodic inspection.A system performance degradation model is established.Preventive maintenance probability and corrective renewal probability models are developed to calculate expected warranty costs and system availability.A benefits function,which includes incentives,is constructed to optimize the initial and subsequent inspection intervals and preventive maintenance thresholds,thereby maximizing warranty profit.An improved sparrow search algorithm is developed to optimize the model,with a case study on large steam turbine rotor shafts.The results suggest the optimal PBW strategy involves an initial inspection interval of approximately 20 months,with subsequent intervals of about four months,and a preventive maintenance threshold of approximately 37.39 mm wear.When compared to common cost-minimization-based condition maintenance strategies and PBW strategies that do not differentiate between initial and subsequent inspection intervals,the proposed PBW strategy increases the manufacturer’s profit by 1%and 18%,respectively.Sensitivity analyses provide managerial recommendations for PBW implementation.The PBW strategy proposed in this study significantly increases manufacturers’profits by optimizing inspection intervals and preventive maintenance thresholds,and manufacturers should focus on technological improvement in preventive maintenance and cost control to further enhance earnings. 展开更多
关键词 performance-based warranty gamma process periodic inspection intelligent 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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Enhanced UAV Pursuit-Evasion Using Boids Modelling:A Synergistic Integration of Bird Swarm Intelligence and DRL 认领 引用 被引量：5
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作者 Weiqiang Jin Xingwu Tian +3 位作者 Bohang Shi Biao Zhao Haibin Duan Hao Wu 《Computers, Materials & Continua》 SCIE EI 2024年第9期3523-3553,共31页
TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving i... TheUAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles(UAVs),which is pivotal in public safety applications,particularly in scenarios involving intrusion monitoring and interception.To address the challenges of data acquisition,real-world deployment,and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks,we propose an innovative swarm intelligencebased UAV pursuit-evasion control framework,namely“Boids Model-based DRL Approach for Pursuit and Escape”(Boids-PE),which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning(DRL).The Boids model,which simulates collective behavior through three fundamental rules,separation,alignment,and cohesion,is adopted in our work.By integrating Boids model with the Apollonian Circles algorithm,significant improvements are achieved in capturing UAVs against simple evasion strategies.To further enhance decision-making precision,we incorporate a DRL algorithm to facilitate more accurate strategic planning.We also leverage self-play training to continuously optimize the performance of pursuit UAVs.During experimental evaluation,we meticulously designed both one-on-one and multi-to-one pursuit-evasion scenarios,customizing the state space,action space,and reward function models for each scenario.Extensive simulations,supported by the PyBullet physics engine,validate the effectiveness of our proposed method.The overall results demonstrate that Boids-PE significantly enhance the efficiency and reliability of UAV pursuit-evasion tasks,providing a practical and robust solution for the real-world application of UAV pursuit-evasion missions. 展开更多
关键词 UAV pursuit-evasion swarm intelligence algorithm Boids model deep reinforcement learning self-play training
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Multi-Hop Routing Optimization Method Based on Improved Ant Algorithm for Vehicle to Roadside Network 认领 引用 被引量：2
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作者 Hao Dong Xiaohui Zhao +2 位作者 Liangdong Qu Xuefen Chi Xinyu Cui 《Journal of Bionic Engineering》 SCIE EI CSCD 2014年第3期490-496,共7页
This paper proposes a route optimization method to improve the performance of route selection in Vehicle Ad-hoc Network(VANET).A novel bionic swarm intelligence algorithm,which is called ant colony algorithm,was intro... This paper proposes a route optimization method to improve the performance of route selection in Vehicle Ad-hoc Network(VANET).A novel bionic swarm intelligence algorithm,which is called ant colony algorithm,was introduced into a traditional ad-hoc route algorithm named AODV.Based on the analysis of movement characteristics of vehicles and according to the spatial relationship between the vehicles and the roadside units,the parameters in ant colony system were modified to enhance the performance of the route selection probability rules.When the vehicle moves into the range of several different roadsides,it could build the route by sending some route testing packets as ants,so that the route table can be built by the reply information of test ants,and then the node can establish the optimization path to send the application packets.The simulation results indicate that the proposed algorithm has better performance than the traditional AODV algorithm,especially when the vehicle is in higher speed or the number of nodes increases. 展开更多
关键词 multi-hop routing optimization ant colony algorithm VANET bionic swarm intelligence algorithm
Research Progress of Photovoltaic Power Prediction Technology Based on Artificial Intelligence Methods 认领 引用 被引量：3
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作者 Daixuan Zhou Yujin Liu +2 位作者 Xu Wang Fuxing Wang Yan Jia 《Energy Engineering》 EI 2024年第12期3573-3616,共44页
With the increasing proportion of renewable energy in China’s energy structure,among which photovoltaic power generation is also developing rapidly.As the photovoltaic(PV)power output is highly unstable and subject t... With the increasing proportion of renewable energy in China’s energy structure,among which photovoltaic power generation is also developing rapidly.As the photovoltaic(PV)power output is highly unstable and subject to a variety of factors,it brings great challenges to the stable operation and dispatch of the power grid.Therefore,accurate short-term PV power prediction is of great significance to ensure the safe grid connection of PV energy.Currently,the short-term prediction of PV power has received extensive attention and research,but the accuracy and precision of the prediction have to be further improved.Therefore,this paper reviews the PV power prediction methods from five aspects:influencing factors,evaluation indexes,prediction status,difficulties and future trends.Then summarizes the current difficulties in prediction based on an in-depth analysis of the current research status of physical methods based on the classification ofmodel features,statistical methods,artificial intelligence methods,and combinedmethods of prediction.Finally,the development trend ofPVpower generation prediction technology and possible future research directions are envisioned. 展开更多
关键词 Photovoltaic power generation power prediction artificial intelligence algorithm
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Structural Optimization of a Multi-Story Frame Structure Based on a Pre-Trained Physics-Informed Neural Network(PINN)Surrogate Model 认领 引用
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作者 An Xu Zhixiong Liu +4 位作者 Hua Rong Liang Han Wei Shi Jun Huang Jiyang Fu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期279-313,共35页
In structural optimization,data-driven surrogate models are often explored as alternatives to finite element analysis to reduce computational cost.However,conventional neural networks usually fail to capture key struc... In structural optimization,data-driven surrogate models are often explored as alternatives to finite element analysis to reduce computational cost.However,conventional neural networks usually fail to capture key structural characteristics and are limited to predicting global responses(e.g.,top displacement),but usually fail to achieve accurate internal force predictions with conventional training data volumes.As a result,most existing studies involving surrogate models did not concern internal force constraints.To address this issue,this study proposes a structural optimization framework based on a pre-trained Physics-Informed Neural Network(PINN)surrogate model.By embedding static equilibrium equation into the loss function,the model achieves higher predictive accuracy,particularly for internal forces,while pre-training accelerates convergence and enhances stability.Combined with an improved multi-swarm particle swarm optimization(MPSO)algorithm,the framework enables efficient optimization of multi-story frame structures under internal force and multiple other constraints.The application to a six-story frame structure validates its effectiveness:compared with a DNN-based model,the PINN-based model improves the coefficient of determination for internal force prediction from 0.8874 to 0.9937.These results demonstrate that the proposed method offers a promising approach for efficient optimization of multi-story frame structures. 展开更多
关键词 Structural optimization intelligent algorithms physics-informed neural networks(PINN) neural networks
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IMLMA:An Intelligent Algorithm for Model Lifecycle Management with Automated Retraining,Versioning,and Monitoring 认领 引用
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作者 Yu Cao Yiyun He Chi Zhang 《Journal of Electronic Research and Application》 2025年第5期233-248,共16页
With the rapid adoption of artificial intelligence(AI)in domains such as power,transportation,and finance,the number of machine learning and deep learning models has grown exponentially.However,challenges such as dela... With the rapid adoption of artificial intelligence(AI)in domains such as power,transportation,and finance,the number of machine learning and deep learning models has grown exponentially.However,challenges such as delayed retraining,inconsistent version management,insufficient drift monitoring,and limited data security still hinder efficient and reliable model operations.To address these issues,this paper proposes the Intelligent Model Lifecycle Management Algorithm(IMLMA).The algorithm employs a dual-trigger mechanism based on both data volume thresholds and time intervals to automate retraining,and applies Bayesian optimization for adaptive hyperparameter tuning to improve performance.A multi-metric replacement strategy,incorporating MSE,MAE,and R2,ensures that new models replace existing ones only when performance improvements are guaranteed.A versioning and traceability database supports comparison and visualization,while real-time monitoring with stability analysis enables early warnings of latency and drift.Finally,hash-based integrity checks secure both model files and datasets.Experimental validation in a power metering operation scenario demonstrates that IMLMA reduces model update delays,enhances predictive accuracy and stability,and maintains low latency under high concurrency.This work provides a practical,reusable,and scalable solution for intelligent model lifecycle management,with broad applicability to complex systems such as smart grids. 展开更多
关键词 Model lifecycle management Intelligent algorithms Hyperparameter optimization Versioning and traceability Power metering
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Performance evaluation and interpretation of hybrid models based on light gradient boosting machine to predict coal mining subsidence 认领 引用
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作者 Xuan Cui Shengli Yang Jiachen Wang 《International Journal of Coal Science & Technology》 SCIE EI CAS CSCD 2026年第1期64-83,共20页
Accurate prediction of coal mining subsidence(CMS)is pivotal for mining design,environmental preservation,safety assurance,and the formulation of preventive measures in mining areas.This study introduces an effective ... Accurate prediction of coal mining subsidence(CMS)is pivotal for mining design,environmental preservation,safety assurance,and the formulation of preventive measures in mining areas.This study introduces an effective hybrid prediction model for coal mining subsidence based on light gradient boosting machine(LightGBM)and uses Shapley Additive Explanations(SHAP)method to reveal and explain the contribution mechanism and interaction of factors affecting mining subsidence.This study collected a dataset of 163 mining subsidence cases(covering 12 features such as coal seams,rock layers,and mining conditions)to model mining subsidence prediction,and evaluated the performance of the model through multiple performance indicators.The results indicate that the hybrid prediction model developed in this paper demonstrates remarkable performance on the test set,with HGS-LightGBM standing out,achieving the coefficient of determination(R2)of 0.9589,while the single LightGBM achieves an R2of 0.925.Finally,apply model interpretation techniques to analyze the impact of input features on mining subsidence,and explain the prediction principles and decision-making process of the model.The analysis reveals that the thickness of coal seam(m)is the most influential parameter for CMS.Furthermore,a targeted interaction analysis on key parameters was conducted to clarify the impact mechanisms of each influencing factor.In summary,the model established in this study has excellent performance and exhibits significant interpretability and transparency. 展开更多
关键词 Coal mining subsidence LightGBM Machine learning Intelligent algorithm Sensitivity analysis
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Recent Advances and Future Directions in Centrifugal Slurry Pump Design Optimization:A Lifecycle-Oriented Review 认领 引用
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作者 Jianping Yuan Chenxin Yu +2 位作者 Yanxia Fu Weidong Wang Heng Liao 《Fluid Dynamics & Materials Processing》 EI 2026年第6期285-312,共28页
Slurry transport is a critical multiphase-flow process in mining,metallurgy,and dredging applications,where hydraulic efficiency,particle-induced wear,cavitation erosion,and structural vibration are strongly coupled.T... Slurry transport is a critical multiphase-flow process in mining,metallurgy,and dredging applications,where hydraulic efficiency,particle-induced wear,cavitation erosion,and structural vibration are strongly coupled.This topic-focused review synthesizes recent advances in centrifugal slurry pump design optimization from the perspectives of wear-resistant surface engineering,hydraulic design,structural dynamics,intelligent optimization algorithms,and multiphysics simulation.Unlike earlier reviews that primarily addressed hydraulic performance,erosion wear,flow visualization,or numerical modeling in isolation,the present work adopts a lifecycle-oriented perspective.Representative studies are critically evaluated according to reported efficiency improvements,wearrate and material-loss reduction,cavitation and net-positive-suction-head-related performance changes,validation strategies,uncertainty sources,and practical engineering feasibility.Particular attention is devoted to the integration of computational fluid dynamics with the discrete element method,fluid-structure interaction,cavitation-erosion coupling,particle-size effects,surrogate-assisted optimization,and digital-twin-enabled monitoring frameworks.The reviewed literature indicates that high-fidelity simulations and intelligent algorithms have significantly enhanced design exploration and predictive capability.However,their large-scale engineering deployment remains limited by challenges associated with model validation,data availability,computational cost,interpretability,and generalization under variable slurry conditions.Finally,digital-twin-enabled lifecycle optimization is discussed as a promising conceptual pathway rather than a fully validated industrial solution,highlighting the need for reduced-order modeling,robust sensing strategies,uncertainty-aware data assimilation,and staged experimental validation to support reliable real-world implementation. 展开更多
关键词 Slurry pump design optimization wear-resistant materials hydraulic model intelligent algorithms multiphysics simulation
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Multi-trial Vector-based Whale Optimization Algorithm 认领 引用 被引量：1
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作者 Mohammad H.Nadimi-Shahraki Hajar Farhanginasab +2 位作者 Shokooh Taghian Ali Safaa Sadiq Seyedali Mirjalili 《Journal of Bionic Engineering》 SCIE EI CSCD 2024年第3期1465-1495,共31页
The Whale Optimization Algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback whales.In spite of its popularity due to simplicity,ease of implementation,and a limited... The Whale Optimization Algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback whales.In spite of its popularity due to simplicity,ease of implementation,and a limited number of parameters,WOA’s search strategy can adversely affect the convergence and equilibrium between exploration and exploitation in complex problems.To address this limitation,we propose a new algorithm called Multi-trial Vector-based Whale Optimization Algorithm(MTV-WOA)that incorporates a Balancing Strategy-based Trial-vector Producer(BS_TVP),a Local Strategy-based Trial-vector Producer(LS_TVP),and a Global Strategy-based Trial-vector Producer(GS_TVP)to address real-world optimization problems of varied degrees of difficulty.MTV-WOA has the potential to enhance exploitation and exploration,reduce the probability of being stranded in local optima,and preserve the equilibrium between exploration and exploitation.For the purpose of evaluating the proposed algorithm's performance,it is compared to eight metaheuristic algorithms utilizing CEC 2018 test functions.Moreover,MTV-WOA is compared with well-stablished,recent,and WOA variant algorithms.The experimental results demonstrate that MTV-WOA surpasses comparative algorithms in terms of the accuracy of the solutions and convergence rate.Additionally,we conducted the Friedman test to assess the gained results statistically and observed that MTV-WOA significantly outperforms comparative algorithms.Finally,we solved five engineering design problems to demonstrate the practicality of MTV-WOA.The results indicate that the proposed MTV-WOA can efficiently address the complexities of engineering challenges and provide superior solutions that are superior to those of other algorithms. 展开更多
关键词 Swarm intelligence algorithms Metaheuristic algorithms Optimization Engineering design problems Whale optimization algorithm
Improvement of the Firework Algorithm for Classification Problems 认领 引用
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作者 Yu Xue Sow Alpha Amadou Yan Zhao 《Journal of Cyber Security》 2020年第4期191-196,共6页
Attracted numerous analysts’consideration,classification is one of the primary issues in Machine learning.Numerous evolutionary algorithms(EAs)were utilized to improve their global search ability.In the previous year... Attracted numerous analysts’consideration,classification is one of the primary issues in Machine learning.Numerous evolutionary algorithms(EAs)were utilized to improve their global search ability.In the previous years,many scientists have attempted to tackle this issue,yet regardless of the endeavors,there are still a few inadequacies.Based on solving the classification problem,this paper introduces a new optimization classification model,which can be applied to the majority of evolutionary computing(EC)techniques.Firework algorithm(FWA)is one of the EC methods,Although the Firework algorithm(FWA)is a proficient algorithm for solving complex optimization issue.The proficient of the FWA isn't fulfilled when being utilized for solving the classification issues.In this paper we previously proposed optimization classification model according to the classification issue.At that point we legitimately utilize the model with FWA to solve the classification issue.Finally,to investigate the performance of our model,we select 4 datasets in the experiments,and the results indicate that an improved FWA can upgrade the classification accuracy by using this model. 展开更多
关键词 Swarm intelligence algorithm firework algorithm evolutionary algorithm classification
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A Novel Control Algorithm for Interaction Between Surface Waves and A Permeable Floating Structure 认领 引用 被引量：1
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作者 Pei-Wei TSAI A.ALSAEDI +1 位作者 T.HAYAT Cheng-Wu CHEN 《China Ocean Engineering》 SCIE EI CSCD 2016年第2期161-176,共16页
An analytical solution is undertaken to describe the wave-induced flow field and the surge motion of a permeable platform structure with fuzzy controllers in an oceanic environment.In the design procedure of the contr... An analytical solution is undertaken to describe the wave-induced flow field and the surge motion of a permeable platform structure with fuzzy controllers in an oceanic environment.In the design procedure of the controller,a parallel distributed compensation（PDC） scheme is utilized to construct a global fuzzy logic controller by blending all local state feedback controllers.A stability analysis is carried out for a real structure system by using Lyapunov method.The corresponding boundary value problems are then incorporated into scattering and radiation problems.They are analytically solved,based on separation of variables,to obtain series solutions in terms of the harmonic incident wave motion and surge motion.The dependence of the wave-induced flow field and its resonant frequency on wave characteristics and structure properties including platform width,thickness and mass has been thus drawn with a parametric approach.From which mathematical models are applied for the wave-induced displacement of the surge motion.A nonlinearly inverted pendulum system is employed to demonstrate that the controller tuned by swarm intelligence method can not only stabilize the nonlinear system,but has the robustness against external disturbance. 展开更多
关键词 fuzzy Lyapunov function Takagi?Sugeno form swarm intelligence algorithm
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Performance evaluation for intelligent optimization algorithms in self-potential data inversion 认领 引用 被引量：7
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作者 崔益安 朱肖雄 +2 位作者 陈志学 刘嘉文 柳建新 《Journal of Central South University》 SCIE EI CAS CSCD 2016年第10期2659-2668,共10页
The self-potential method is widely used in environmental and engineering geophysics. Four intelligent optimization algorithms are adopted to design the inversion to interpret self-potential data more accurately and e... The self-potential method is widely used in environmental and engineering geophysics. Four intelligent optimization algorithms are adopted to design the inversion to interpret self-potential data more accurately and efficiently: simulated annealing, genetic, particle swarm optimization, and ant colony optimization. Using both noise-free and noise-added synthetic data, it is demonstrated that all four intelligent algorithms can perform self-potential data inversion effectively. During the numerical experiments, the model distribution in search space, the relative errors of model parameters, and the elapsed time are recorded to evaluate the performance of the inversion. The results indicate that all the intelligent algorithms have good precision and tolerance to noise. Particle swarm optimization has the fastest convergence during iteration because of its good balanced searching capability between global and local minimisation. 展开更多
关键词 self-potential inversion intelligent algorithm
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Review on Application of Artificial Intelligence in Civil Engineering 认领 引用 被引量：7
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作者 Youqin Huang Jiayong Li Jiyang Fu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2019年第12期845-875,共31页
In last few years,big data and deep learning technologies have been successfully applied in various fields of civil engineering with the great progress of machine learning techniques.However,until now,there has been n... In last few years,big data and deep learning technologies have been successfully applied in various fields of civil engineering with the great progress of machine learning techniques.However,until now,there has been no comprehensive review on its applications in civil engineering.To fill this gap,this paper reviews the application and development of artificial intelligence in civil engineering in recent years,including intelligent algorithms,big data and deep learning.Through the work of this paper,the research direction and difficulties of artificial intelligence in civil engineering for the past few years can be known.It is shown that the studies of artificial intelligence in civil engineering mainly focus on structural maintenance and management,and the design optimization. 展开更多
关键词 Artificial intelligence civil engineering intelligent algorithms big data deep learning structural maintenance
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INTELLIGENT ALGORITHMS FOR SOLVING CNOP AND THEIR APPLICATIONS IN ENSO PREDICTABILITY AND TROPICAL CYCLONE ADAPTIVE OBSERVATIONS 认领 引用 被引量：5
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作者 MU Bin ZHANG Lin-lin +4 位作者 YUAN Shi-jin QIAN Yi-wen WEN Shi-cheng YAN Jing-hao ZHOU Fei-fan 《Journal of Tropical Meteorology》 SCIE 2019年第1期63-81,共19页
Some intelligent algorithms(IAs) proposed by us, including swarm IAs and single individual IAs, have been applied to the Zebiak-Cane(ZC) model to solve conditional nonlinear optimal perturbation(CNOP) for studying El ... Some intelligent algorithms(IAs) proposed by us, including swarm IAs and single individual IAs, have been applied to the Zebiak-Cane(ZC) model to solve conditional nonlinear optimal perturbation(CNOP) for studying El Ni?o-Southern Oscillation(ENSO) predictability. Compared to the adjoint-based method(the ADJ-method), which is referred to as a benchmark, these IAs can achieve approximate CNOP results in terms of magnitudes and patterns.Using IAs to solve CNOP can avoid the use of an adjoint model and widen the application of CNOP in numerical climate and weather modeling. Of the proposed swarm IAs, PCA-based particle swarm optimization(PPSO) obtains CNOPs with the best patterns and the best stability. Of the proposed single individual IAs, continuous tabu search algorithm with sine maps and staged strategy(CTS-SS) has the highest efficiency. In this paper, we compare the validity, stability and efficiency of parallel PPSO and CTS-SS using these two IAs to solve CNOP in the ZC model for studying ENSO predictability. The experimental results show that CTS-SS outperforms parallel PPSO except with respect to stability. At the same time, we are also concerned with whether these two IAs can effectively solve CNOP when applied to more complicated models. Taking the sensitive areas identification of tropical cyclone adaptive observations as an example and using the fifth-generation mesoscale model(MM5), we design some experiments. The experimental results demonstrate that each of these two IAs can effectively solve CNOP and that parallel PPSO has a higher efficiency than CTS-SS. We also provide some suggestions on how to choose a suitable IA to solve CNOP for different models. 展开更多
关键词 CNOP single individual intelligent algorithms swarm intelligent algorithms ENSO predictability tropical cyclone adaptive observations
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