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Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation
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作者 Adel Binbusayyis Mohemmed Sha 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第1期909-931,共23页
Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart ... Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system. 展开更多
关键词 Smart Grid machine learning particle swarm optimization XGBoost dynamic inertia weight update
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A Novel Vertical Handoff Algorithm with Dynamic Weights in Wireless Heterogeneous Networks 被引量:2
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作者 Liu Xia Zhao Fangming Jiang Lingge 《China Communications》 SCIE CSCD 2010年第5期70-76,共7页
In order to enhance the quality of vertical handoff in an overlay wireless network, multiple attributes are taken into account when optimizing the vertical handoff decision including user-based and network-based QoS f... In order to enhance the quality of vertical handoff in an overlay wireless network, multiple attributes are taken into account when optimizing the vertical handoff decision including user-based and network-based QoS factors. In this paper, we develop a novel vertical handoff algorithm in an integrated 3G cellular and Wireless LAN networks. The proposed algorithm can adjust the weight of each QoS attribute dynamically as the networks change, trace the network condition and choose the optimal access point at transient regions. Simulation results show that this algorithm is able to provide accurate handoff decision, resulting in small unnecessary handoff numbers, good performance of throughput and handoff delay in heterogeneous environments. 展开更多
关键词 vertical handoff multiple attrbute dynamic weight heterogeneous networks
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Distributed Filtering Algorithm Based on Tunable Weights Under Untrustworthy Dynamics 被引量:1
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作者 Shiming Chen Xiaoling Chen +2 位作者 Zhengkai Pei Xingxing Zhang Huajing Fang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI 2016年第2期225-232,共8页
Aiming at effective fusion of a system state estimate of sensor network under attack in an untrustworthy environment, distributed filtering algorithm based on tunable weights is proposed. Considering node location and... Aiming at effective fusion of a system state estimate of sensor network under attack in an untrustworthy environment, distributed filtering algorithm based on tunable weights is proposed. Considering node location and node influence over the network topology, a distributed filtering algorithm is developed to evaluate the certainty degree firstly. Using the weight reallocation approach, the weights of the attacked nodes are assigned to other intact nodes to update the certainty degree, and then the weight composed by the certainty degree is used to optimize the consensus protocol to update the node estimates. The proposed algorithm not only improves accuracy of the distributed filtering, but also enhances consistency of the node estimates. Simulation results demonstrate the effectiveness of the proposed algorithm. © 2014 Chinese Association of Automation. 展开更多
关键词 Data fusion Sensor networks Signal filtering and prediction
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Dynamic Location Method for Shallow Ocean Bottom Nodes Using the Levenberg-Marquart Algorithm
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作者 TONG Siyou LI Junjie +2 位作者 XU Xiugang FANG Yunfen WANG Zhongcheng 《Journal of Ocean University of China》 SCIE CAS CSCD 2024年第4期953-960,共8页
Ocean bottom node(OBN)data acquisition is the main development direction of marine seismic exploration;it is widely promoted,especially in shallow sea environments.However,the OBN receivers may move several times beca... Ocean bottom node(OBN)data acquisition is the main development direction of marine seismic exploration;it is widely promoted,especially in shallow sea environments.However,the OBN receivers may move several times because they are easily affected by tides,currents,and other factors in the shallow sea environment during long-term acquisition.If uncorrected,then the imaging quality of subsequent processing will be affected.The conventional secondary positioning does not consider the case of multiple movements of the receivers,and the accuracy of secondary positioning is insufficient.The first arrival wave of OBN seismic data in shallow ocean mainly comprises refracted waves.In this study,a nonlinear model is established in accordance with the propagation mechanism of a refracted wave and its relationship with the time interval curve to realize the accurate location of multiple receiver movements.In addition,the Levenberg-Marquart algorithm is used to reduce the influence of the first arrival pickup error and to automatically detect the receiver movements,identifying the accurate dynamic relocation of the receivers.The simulation and field data show that the proposed method can realize the dynamic location of multiple receiver movements,thereby improving the accuracy of seismic imaging and achieving high practical value. 展开更多
关键词 OBN dynamic location method Levenberg-Marquart algorithm seismic exploration of shallow sea
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MADDPG-D2: An Intelligent Dynamic Task Allocation Algorithm Based on Multi-Agent Architecture Driven by Prior Knowledge
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作者 Tengda Li Gang Wang Qiang Fu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第9期2559-2586,共28页
Aiming at the problems of low solution accuracy and high decision pressure when facing large-scale dynamic task allocation(DTA)and high-dimensional decision space with single agent,this paper combines the deep reinfor... Aiming at the problems of low solution accuracy and high decision pressure when facing large-scale dynamic task allocation(DTA)and high-dimensional decision space with single agent,this paper combines the deep reinforce-ment learning(DRL)theory and an improved Multi-Agent Deep Deterministic Policy Gradient(MADDPG-D2)algorithm with a dual experience replay pool and a dual noise based on multi-agent architecture is proposed to improve the efficiency of DTA.The algorithm is based on the traditional Multi-Agent Deep Deterministic Policy Gradient(MADDPG)algorithm,and considers the introduction of a double noise mechanism to increase the action exploration space in the early stage of the algorithm,and the introduction of a double experience pool to improve the data utilization rate;at the same time,in order to accelerate the training speed and efficiency of the agents,and to solve the cold-start problem of the training,the a priori knowledge technology is applied to the training of the algorithm.Finally,the MADDPG-D2 algorithm is compared and analyzed based on the digital battlefield of ground and air confrontation.The experimental results show that the agents trained by the MADDPG-D2 algorithm have higher win rates and average rewards,can utilize the resources more reasonably,and better solve the problem of the traditional single agent algorithms facing the difficulty of solving the problem in the high-dimensional decision space.The MADDPG-D2 algorithm based on multi-agent architecture proposed in this paper has certain superiority and rationality in DTA. 展开更多
关键词 Deep reinforcement learning dynamic task allocation intelligent decision-making multi-agent system MADDPG-D2 algorithm
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Structure-preserving algorithms for guiding center dynamics based on the slow manifold of classical Pauli particle
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作者 张若涵 王正汹 +1 位作者 肖建元 王丰 《Plasma Science and Technology》 SCIE EI CAS CSCD 2024年第6期88-102,共15页
The classical Pauli particle(CPP) serves as a slow manifold, substituting the conventional guiding center dynamics. Based on the CPP, we utilize the averaged vector field(AVF) method in the computations of drift orbit... The classical Pauli particle(CPP) serves as a slow manifold, substituting the conventional guiding center dynamics. Based on the CPP, we utilize the averaged vector field(AVF) method in the computations of drift orbits. Demonstrating significantly higher efficiency, this advanced method is capable of accomplishing the simulation in less than one-third of the time of directly computing the guiding center motion. In contrast to the CPP-based Boris algorithm, this approach inherits the advantages of the AVF method, yielding stable trajectories even achieved with a tenfold time step and reducing the energy error by two orders of magnitude. By comparing these two CPP algorithms with the traditional RK4 method, the numerical results indicate a remarkable performance in terms of both the computational efficiency and error elimination. Moreover, we verify the properties of slow manifold integrators and successfully observe the bounce on both sides of the limiting slow manifold with deliberately chosen perturbed initial conditions. To evaluate the practical value of the methods, we conduct simulations in non-axisymmetric perturbation magnetic fields as part of the experiments,demonstrating that our CPP-based AVF method can handle simulations under complex magnetic field configurations with high accuracy, which the CPP-based Boris algorithm lacks. Through numerical experiments, we demonstrate that the CPP can replace guiding center dynamics in using energy-preserving algorithms for computations, providing a new, efficient, as well as stable approach for applying structure-preserving algorithms in plasma simulations. 展开更多
关键词 structure-preserving algorithm averaged vector field classical Pauli particle guiding center dynamics
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Multi-Label Feature Selection Based on Improved Ant Colony Optimization Algorithm with Dynamic Redundancy and Label Dependence
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作者 Ting Cai Chun Ye +5 位作者 Zhiwei Ye Ziyuan Chen Mengqing Mei Haichao Zhang Wanfang Bai Peng Zhang 《Computers, Materials & Continua》 SCIE EI 2024年第10期1157-1175,共19页
The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challengi... The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging.Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features.The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement learning.Nevertheless,existing methods do not consider crucial correlation information,such as dynamic redundancy and label correlation.To tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label correlation.Initially,the dynamic redundancy is assessed between the selected feature subset and potential features.Meanwhile,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label weights.Experimental results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper. 展开更多
关键词 Multi-label feature selection ant colony optimization algorithm dynamic redundancy high-dimensional data label correlation
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Vehicle routing optimization algorithm based on time windows and dynamic demand
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作者 LI Jun DUAN Yurong +1 位作者 ZHANG Weiwei ZHU Liyuan 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2024年第3期369-378,共10页
To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand,customer cancellation service,and change of customer delivery address,... To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand,customer cancellation service,and change of customer delivery address,based on the ideas of pre-optimization and real-time optimization,a two-stage planning model of dynamic demand based vehicle routing problem with time windows was established.At the pre-optimization stage,an improved genetic algorithm was used to obtain the pre-optimized distribution route,a large-scale neighborhood search method was integrated into the mutation operation to improve the local optimization performance of the genetic algorithm,and a variety of operators were introduced to expand the search space of neighborhood solutions;At the real-time optimization stage,a periodic optimization strategy was adopted to transform a complex dynamic problem into several static problems,and four neighborhood search operators were used to quickly adjust the route.Two different scale examples were designed for experiments.It is proved that the algorithm can plan the better route,and adjust the distribution route in time under the real-time constraints.Therefore,the proposed algorithm can provide theoretical guidance for suppliers to solve the dynamic demand based vehicle routing problem. 展开更多
关键词 vehicle routing problem dynamic demand genetic algorithm large-scale neighborhood search time windows
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Multimodel Ensemble Forecast of Global Horizontal Irradiance at PV Power Stations Based on Dynamic Variable Weight
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作者 YUAN Bin SHEN Yan-bo +6 位作者 DENG Hua YANG Yang CHEN Qi-ying YE Dong MO Jing-yue YAO Jin-feng LIU Zong-hui 《Journal of Tropical Meteorology》 SCIE 2024年第3期327-336,共10页
In the present study,multimodel ensemble forecast experiments of the global horizontal irradiance(GHI)were conducted using the dynamic variable weight technique.The study was based on the forecasts of four numerical m... In the present study,multimodel ensemble forecast experiments of the global horizontal irradiance(GHI)were conducted using the dynamic variable weight technique.The study was based on the forecasts of four numerical models,namely,the China Meteorological Administration Wind Energy and Solar Energy Prediction System,the Mesoscale Weather Numerical Prediction System of China Meteorological Administration,the China Meteorological Administration Regional Mesoscale Numerical Prediction System-Guangdong,and the Weather Research and Forecasting Model-Solar,and observational data from four photovoltaic(PV)power stations in Yangjiang City,Guangdong Province.The results show that compared with those of the monthly optimal numerical model forecasts,the dynamic variable weight-based ensemble forecasts exhibited 0.97%-15.96%smaller values of the mean absolute error and 3.31%-18.40%lower values of the root mean square error(RMSE).However,the increase in the correlation coefficient was not obvious.Specifically,the multimodel ensemble mainly improved the performance of GHI forecasts below 700 W m^(-2),particularly below 400 W m^(-2),with RMSE reductions as high as 7.56%-28.28%.In contrast,the RMSE increased at GHI levels above 700 W m^(-2).As for the key period of PV power station output(02:00-07:00),the accuracy of GHI forecasts could be improved by the multimodel ensemble:the multimodel ensemble could effectively decrease the daily maximum absolute error(AE max)of GHI forecasts.Moreover,with increasing forecasting difficulty under cloudy conditions,the multimodel ensemble,which yields data closer to the actual observations,could simulate GHI fluctuations more accurately. 展开更多
关键词 GHI forecast multimodel ensemble dynamic variable weight PV power station
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Enhanced asphalt dynamic modulus prediction: A detailed analysis of artificial hummingbird algorithm-optimised boosted trees
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作者 Ikenna D.Uwanuakwa Ilham Yahya Amir Lyce Ndolo Umba 《Journal of Road Engineering》 2024年第2期224-233,共10页
This study introduces and evaluates a novel artificial hummingbird algorithm-optimised boosted tree(AHAboosted)model for predicting the dynamic modulus(E*)of hot mix asphalt concrete.Using a substantial dataset from N... This study introduces and evaluates a novel artificial hummingbird algorithm-optimised boosted tree(AHAboosted)model for predicting the dynamic modulus(E*)of hot mix asphalt concrete.Using a substantial dataset from NCHRP Report-547,the model was trained and rigorously tested.Performance metrics,specifically RMSE,MAE,and R2,were employed to assess the model's predictive accuracy,robustness,and generalisability.When benchmarked against well-established models like support vector machines(SVM)and gaussian process regression(GPR),the AHA-boosted model demonstrated enhanced performance.It achieved R2 values of 0.997 in training and 0.974 in testing,using the traditional Witczak NCHRP 1-40D model inputs.Incorporating features such as test temperature,frequency,and asphalt content led to a 1.23%increase in the test R2,signifying an improvement in the model's accuracy.The study also explored feature importance and sensitivity through SHAP and permutation importance plots,highlighting binder complex modulus|G*|as a key predictor.Although the AHA-boosted model shows promise,a slight decrease in R2 from training to testing indicates a need for further validation.Overall,this study confirms the AHA-boosted model as a highly accurate and robust tool for predicting the dynamic modulus of hot mix asphalt concrete,making it a valuable asset for pavement engineering. 展开更多
关键词 ASPHALT dynamic modulus PREDICTION Artificial hummingbird algorithm Boosted tree
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Systematic Review:Load Balancing in Cloud Computing by Using Metaheuristic Based Dynamic Algorithms
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作者 Darakhshan Syed Ghulam Muhammad Safdar Rizvi 《Intelligent Automation & Soft Computing》 2024年第3期437-476,共40页
Cloud Computing has the ability to provide on-demand access to a shared resource pool.It has completely changed the way businesses are managed,implement applications,and provide services.The rise in popularity has led... Cloud Computing has the ability to provide on-demand access to a shared resource pool.It has completely changed the way businesses are managed,implement applications,and provide services.The rise in popularity has led to a significant increase in the user demand for services.However,in cloud environments efficient load balancing is essential to ensure optimal performance and resource utilization.This systematic review targets a detailed description of load balancing techniques including static and dynamic load balancing algorithms.Specifically,metaheuristic-based dynamic load balancing algorithms are identified as the optimal solution in case of increased traffic.In a cloud-based context,this paper describes load balancing measurements,including the benefits and drawbacks associated with the selected load balancing techniques.It also summarizes the algorithms based on implementation,time complexity,adaptability,associated issue(s),and targeted QoS parameters.Additionally,the analysis evaluates the tools and instruments utilized in each investigated study.Moreover,comparative analysis among static,traditional dynamic and metaheuristic algorithms based on response time by using the CloudSim simulation tool is also performed.Finally,the key open problems and potential directions for the state-of-the-art metaheuristic-based approaches are also addressed. 展开更多
关键词 Cloud computing load balancing metaheuristic algorithm dynamic algorithm load balancer QOS
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Dynamic Pricing Strategies Using Artificial Intelligence Algorithm
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作者 Murat Basal Emel Saraç Kadir Özer 《Open Journal of Applied Sciences》 2024年第8期1963-1978,共16页
Pricing strategies can have a huge impact on a company’s success. This paper focuses on the advantages and disadvantages of using artificial intelligence in dynamic pricing strategies. A good understanding of the pos... Pricing strategies can have a huge impact on a company’s success. This paper focuses on the advantages and disadvantages of using artificial intelligence in dynamic pricing strategies. A good understanding of the possible benefits and challenges will help companies to understand the impact of their chosen pricing strategies. AI-driven Dynamic pricing has great opportunities to increase a firm’s profits. Firms can benefit from personalized pricing based on personal behavior and characteristics, as well as cost reduction by increasing efficiency and reducing the need to use manual work and automation. However, AI-driven dynamic rewarding can have a negative impact on customers’ perception of trust, fairness and transparency. Since price discrimination is used, ethical issues such as privacy and equity may arise. Understanding the businesses and customers that determine pricing strategy is so important that one cannot exist without the other. It will provide a comprehensive overview of the main advantages and disadvantages of AI-assisted dynamic pricing strategy. The main objective of this research is to uncover the most notable advantages and disadvantages of implementing AI-enabled dynamic pricing strategies. Future research can extend the understanding of algorithmic pricing through case studies. In this way, new, practical implications can be developed in the future. It is important to investigate how issues related to customers’ trust and feelings of unfairness can be mitigated, for example by price framing. 展开更多
关键词 Artificial Intelligence algorithm dynamic PRICING STRATEGY
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Dynamic Optimization Method on Electromechanical Coupling System by Exponential Inertia Weight Particle Swarm Algorithm 被引量:4
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作者 LI Qiang WU Jianxin SUN Yan 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2009年第4期602-607,共6页
Dynamic optimization of electromechanical coupling system is a significant engineering problem in the field of mechatronics. The performance improvement of electromechanical equipment depends on the system design para... Dynamic optimization of electromechanical coupling system is a significant engineering problem in the field of mechatronics. The performance improvement of electromechanical equipment depends on the system design parameters. Aiming at the spindle unit of refitted machine tool for solid rocket, the vibration acceleration of tool is taken as objective function, and the electromechanical system design parameters are appointed as design variables. Dynamic optimization model is set up by adopting Lagrange-Maxwell equations, Park transform and electromechanical system energy equations. In the procedure of seeking high efficient optimization method, exponential function is adopted to be the weight function of particle swarm optimization algorithm. Exponential inertia weight particle swarm algorithm(EPSA), is formed and applied to solve the dynamic optimization problem of electromechanical system. The probability density function of EPSA is presented and used to perform convergence analysis. After calculation, the optimized design parameters of the spindle unit are obtained in limited time period. The vibration acceleration of the tool has been decreased greatly by the optimized design parameters. The research job in the paper reveals that the problem of dynamic optimization of electromechanical system can be solved by the method of combining system dynamic analysis with reformed swarm particle optimizati on. Such kind of method can be applied in the design of robots, NC machine, and other electromechanical equipments. 展开更多
关键词 particle swarm algorithm electromechanical coupling system dynamic optimization
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A random forest algorithm based on similarity measure and dynamic weighted voting 被引量:1
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作者 ZHAO Shu-xu MA Qin-jing LIU Li-jiao 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2019年第3期277-284,共8页
The random forest model is universal and easy to understand, which is often used for classification and prediction. However, it uses non-selective integration and the majority rule to judge the final result, thus the ... The random forest model is universal and easy to understand, which is often used for classification and prediction. However, it uses non-selective integration and the majority rule to judge the final result, thus the difference between the decision trees in the model is ignored and the prediction accuracy of the model is reduced. Taking into consideration these defects, an improved random forest model based on confusion matrix (CM-RF)is proposed. The decision tree cluster is selectively constructed by the similarity measure in the process of constructing the model, and the result is output by using the dynamic weighted voting fusion method in the final voting session. Experiments show that the proposed CM-RF can reduce the impact of low-performance decision trees on the output result, thus improving the accuracy and generalization ability of random forest model. 展开更多
关键词 random forest confusion matrix similarity measure dynamic weighted voting
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General dynamic model for educational assembling-type robot and its fast simulation algorithm 被引量:2
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作者 高海涛 张志胜 +1 位作者 曹杰 史金飞 《Journal of Southeast University(English Edition)》 EI CAS 2009年第3期340-345,共6页
To realize automatic modeling and dynamic simulation of the educational assembling-type robot with open structure, a general dynamic model for the educational assembling-type robot and a fast simulation algorithm are ... To realize automatic modeling and dynamic simulation of the educational assembling-type robot with open structure, a general dynamic model for the educational assembling-type robot and a fast simulation algorithm are put forward. First, the educational robot system is abstracted to a multibody system and a general dynamic model of the educational robot is constructed by the Newton-Euler method. Then the dynamic model is simplified by a combination of components with fixed connections according to the structural characteristics of the educational robot. Secondly, in order to obtain a high efficiency simulation algorithm, based on the sparse matrix technique, the augmentation algorithm and the direct projective constraint stabilization algorithm are improved. Finally, a numerical example is given. The results show that the model and the fast algorithm are valid and effective. This study lays a dynamic foundation for realizing the simulation platform of the educational robot. 展开更多
关键词 educational robot dynamic model sparse matrix augmentation algorithm constraint stabilization
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Multi-objective integrated optimization based on evolutionary strategy with a dynamic weighting schedule 被引量:2
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作者 傅武军 朱昌明 叶庆泰 《Journal of Southeast University(English Edition)》 EI CAS 2006年第2期204-207,共4页
The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system perf... The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality (LMI) or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method. 展开更多
关键词 integrated design multi-objective optimization evolutionary strategy dynamic weighting schedule suspension system
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Rolling horizon scheduling algorithm for dynamic vehicle scheduling system 被引量:1
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作者 贾永基 谷寒雨 席裕庚 《Journal of Southeast University(English Edition)》 EI CAS 2005年第1期92-96,共5页
Dynamic exclusive pickup and delivery problem with time windows (DE-PDPTW), aspecial dynamic vehicle scheduling problem, is proposed. Its mathematical description is given andits static properties are analyzed, and th... Dynamic exclusive pickup and delivery problem with time windows (DE-PDPTW), aspecial dynamic vehicle scheduling problem, is proposed. Its mathematical description is given andits static properties are analyzed, and then the problem is simplified asthe asymmetrical travelingsalesman problem with time windows. The rolling horizon scheduling algorithm (RHSA) to solve thisdynamic problem is proposed. By the rolling of time horizon, the RHSA can adapt to the problem'sdynamic change and reduce the computation time by dealing with only part of the customers in eachrolling time horizon. Then, its three factors, the current customer window, the scheduling of thecurrent customer window and the rolling strategy, are analyzed. The test results demonstrate theeffectiveness of the RHSA to solve the dynamic vehicle scheduling problem. 展开更多
关键词 dynamic vehicle scheduling rolling horizon scheduling algorithm EXCLUSIVE pickup and delivery problem with time windows (PDPTW)
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Dynamic weighted voting for multiple classifier fusion:a generalized rough set method 被引量:9
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作者 Sun Liang Han Chongzhao 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2006年第3期487-494,共8页
To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting (KD-DWV) is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to ... To improve the performance of multiple classifier system, a knowledge discovery based dynamic weighted voting (KD-DWV) is proposed based on knowledge discovery. In the method, all base classifiers may be allowed to operate in different measurement/feature spaces to make the most of diverse classification information. The weights assigned to each output of a base classifier are estimated by the separability of training sample sets in relevant feature space. For this purpose, some decision tables (DTs) are established in terms of the diverse feature sets. And then the uncertainty measures of the separability are induced, in the form of mass functions in Dempster-Shafer theory (DST), from each DTs based on generalized rough set model. From the mass functions, all the weights are calculated by a modified heuristic fusion function and assigned dynamically to each classifier varying with its output. The comparison experiment is performed on the hyperspectral remote sensing images. And the experimental results show that the performance of the classification can be improved by using the proposed method compared with the plurality voting (PV). 展开更多
关键词 multiple classifier fusion dynamic weighted voting generalized rough set hyperspectral.
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Dynamic threshold for SPWVD parameter estimation based on Otsu algorithm 被引量:11
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作者 Ning Ma Jianxin Wang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2013年第6期919-924,共6页
Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation (LFM) signals. The smoothed pseudo Winger-Ville distribution (SPWVD) is used for the parameter estima... Time-frequency-based methods are proven to be effective for parameter estimation of linear frequency modulation (LFM) signals. The smoothed pseudo Winger-Ville distribution (SPWVD) is used for the parameter estimation of multi-LFM signals, and a method of the SPWVD binarization by a dynamic threshold based on the Otsu algorithm is proposed. The proposed method is effective in the demand for the estimation of different parameters and the unknown signal-to-noise ratio (SNR) circumstance. The performance of this method is confirmed by numerical simulation. 展开更多
关键词 parameter estimation smoothed pseudo Winger-Ville distribution (SPWVD) dynamic threshold Otsu algorithm
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A Hybrid Immigrants Scheme for Genetic Algorithms in Dynamic Environments 被引量:9
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作者 Shengxiang Yang Renato Tinós 《International Journal of Automation and computing》 EI 2007年第3期243-254,共12页
Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the ... Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution(s) of a fixed problem but to track the moving optimum over time. Dynamic optimization problems have attracted a growing interest from the genetic algorithm community in recent years. Several approaches have been developed to enhance the performance of genetic algorithms in dynamic environments. One approach is to maintain the diversity of the population via random immigrants. This paper proposes a hybrid immigrants scheme that combines the concepts of elitism, dualism and random immigrants for genetic algorithms to address dynamic optimization problems. In this hybrid scheme, the best individual, i.e., the elite, from the previous generation and its dual individual are retrieved as the bases to create immigrants via traditional mutation scheme. These elitism-based and dualism-based immigrants together with some random immigrants are substituted into the current population, replacing the worst individuals in the population. These three kinds of immigrants aim to address environmental changes of slight, medium and significant degrees respectively and hence efficiently adapt genetic algorithms to dynamic environments that are subject to different severities of changes. Based on a series of systematically constructed dynamic test problems, experiments are carried out to investigate the performance of genetic algorithms with the hybrid immigrants scheme and traditional random immigrants scheme. Experimental results validate the efficiency of the proposed hybrid immigrants scheme for improving the performance of genetic algorithms in dynamic environments. 展开更多
关键词 Genetic algorithms random immigrants elitism-based immigrants DUALISM dynamic optimization problems.
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