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Novel two⁃stage preflow algorithm for solving the maximum flow problem in a network with circles 认领 引用 被引量：1
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作者 DANG Yaoguo HUANG Jinxin +1 位作者 DING Xiaoyu WANG Junjie 《Journal of Southeast University(English Edition)》 EI CAS 2025年第1期91-100,共10页
The presence of circles in the network maximum flow problem increases the complexity of the preflow algorithm.This study proposes a novel two-stage preflow algorithm to address this issue.First,this study proves that ... The presence of circles in the network maximum flow problem increases the complexity of the preflow algorithm.This study proposes a novel two-stage preflow algorithm to address this issue.First,this study proves that at least one zero-flow arc must be present when the flow of the network reaches its maximum value.This result indicates that the maximum flow of the network will remain constant if a zero-flow arc within a circle is removed;therefore,the maximum flow of each network without circles can be calculated.The first stage involves identifying the zero-flow arc in the circle when the network flow reaches its maximum.The second stage aims to remove the zero-flow arc identified and modified in the first stage,thereby producing a new network without circles.The maximum flow of the original looped network can be obtained by solving the maximum flow of the newly generated acyclic network.Finally,an example is provided to demonstrate the validity and feasibility of this algorithm.This algorithm not only improves computational efficiency but also provides new perspectives and tools for solving similar network optimization problems. 展开更多
关键词 network with circles maximum flow zeroflow arc two-stage preflow algorithm
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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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An improved fluid flow algorithm for hydraulic fracturing:Optimizing domain volume and crack pressure update strategies 认领 引用 被引量：2
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作者 Wei Zhang Jing Bi +3 位作者 Yu Zhao Yongfa Zhang Chaolin Wang Yang Pan 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2025年第4期639-657,共19页
With the widespread adoption of hydraulic fracturing technology in oil and gas resource development,improving the accuracy and efficiency of fracturing simulations has become a critical research focus.This paper propo... With the widespread adoption of hydraulic fracturing technology in oil and gas resource development,improving the accuracy and efficiency of fracturing simulations has become a critical research focus.This paper proposes an improved fluid flow algorithm,aiming to enhance the computational efficiency of hydraulic fracturing simulations while ensuring computational accuracy.The algorithm optimizes the aperture law and iteration criteria,focusing on improving the domain volume and crack pressure update strategy,thereby enabling precise capture of dynamic borehole pressure variations during injection tests.The effectiveness of the algorithm is verified through three flow-solid coupling cases.The study also analyzes the effects of borehole size,domain volume,and crack pressure update strategy on fracturing behavior.Furthermore,the performance of the improved algorithm in terms of crack propagation rate,micro-crack formation,and fluid pressure distribution was further evaluated.The results indicate that while large-size boreholes delay crack initiation,the cracks propagate more rapidly once formed.Additionally,the optimized domain volume calculation and crack pressure update strategy significantly shorten the pressure propagation stage,promote crack propagation,and improve computational efficiency. 展开更多
关键词 Hydraulic fracturing Fluid flow algorithm Domain volume optimization Crack pressure update Borehole size
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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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Enhanced physics-inspired algorithm for optimal power flow with renewable energy integration using Coulomb’s and Franklin’s law under climate considerations 认领 引用
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作者 Saeid Jowkar Amin Besharatiyan +5 位作者 Ali Esmaeel Nezhad Ehsan Rahimi Fariba Esmaeilnezhad Toktam Tavakkoli Sabour Mohammadamin Mobtahej Afshin Canani 《Global Energy Interconnection》 EI CSCD 2025年第6期982-996,共15页
Due to the climate-dependent nature of renewable energy sources(RESs),solving the optimal power flow(OPF)problem in power systems that integrate RESs,such as photovoltaic(PV)units and wind turbines(WTs),remains a sign... Due to the climate-dependent nature of renewable energy sources(RESs),solving the optimal power flow(OPF)problem in power systems that integrate RESs,such as photovoltaic(PV)units and wind turbines(WTs),remains a significant challenge.To address this problem,this study presents an effective framework that incorporates solar and wind power generation.To manage the nonconvex and nonlinear characteristics of the OPF problem,a modified physics-inspired algorithm termed the Enhanced Coulomb’s and Franklin’s laws Algorithm(ECFA),is deployed.In the proposed OPF model,the power generated from RESs is considered a dependent variable,while voltages at buses equipped with RESs serve as decision variables.Real-time data on solar irradiation and wind speed are used to model the power outputs of PV units and WTs,respectively.Although the Coulomb’s and Franklin’s law algorithm(CFA)offers some advantages,it underperforms on complex optimization tasks compared to SSA,BA,SCA,ABC,and CFA.The enhanced version of the CFA improves the search process across the feasible space by incorporating diverse interaction methods and enhancing exploitation capabilities.The performance of the proposed ECFA is assessed through comprehensive comparisons with state-of-the-art methods for solving the OPF problem. 展开更多
关键词 Optimal power flow Coulomb's and Franklin's laws physics-inspired algorithm Wind power Solar power
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An Iterated Greedy Algorithm with Memory and Learning Mechanisms for the Distributed Permutation Flow Shop Scheduling Problem 认领 引用
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作者 Binhui Wang Hongfeng Wang 《Computers, Materials & Continua》 SCIE EI 2025年第1期371-388,共18页
The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because o... The distributed permutation flow shop scheduling problem(DPFSP)has received increasing attention in recent years.The iterated greedy algorithm(IGA)serves as a powerful optimizer for addressing such a problem because of its straightforward,single-solution evolution framework.However,a potential draw-back of IGA is the lack of utilization of historical information,which could lead to an imbalance between exploration and exploitation,especially in large-scale DPFSPs.As a consequence,this paper develops an IGA with memory and learning mechanisms(MLIGA)to efficiently solve the DPFSP targeted at the mini-malmakespan.InMLIGA,we incorporate a memory mechanism to make a more informed selection of the initial solution at each stage of the search,by extending,reconstructing,and reinforcing the information from previous solutions.In addition,we design a twolayer cooperative reinforcement learning approach to intelligently determine the key parameters of IGA and the operations of the memory mechanism.Meanwhile,to ensure that the experience generated by each perturbation operator is fully learned and to reduce the prior parameters of MLIGA,a probability curve-based acceptance criterion is proposed by combining a cube root function with custom rules.At last,a discrete adaptive learning rate is employed to enhance the stability of the memory and learningmechanisms.Complete ablation experiments are utilized to verify the effectiveness of the memory mechanism,and the results show that this mechanism is capable of improving the performance of IGA to a large extent.Furthermore,through comparative experiments involving MLIGA and five state-of-the-art algorithms on 720 benchmarks,we have discovered that MLI-GA demonstrates significant potential for solving large-scale DPFSPs.This indicates that MLIGA is well-suited for real-world distributed flow shop scheduling. 展开更多
关键词 Distributed permutation flow shop scheduling makespan iterated greedy algorithm memory mechanism cooperative reinforcement learning
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Probabilistic Assessment of PV-DG for Optimal Multi-Locations and Sizing Using Genetic Algorithm and Sequential-Time Power Flow 认领 引用
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作者 A. Elkholy 《Journal of Power and Energy Engineering》 2025年第2期23-42,共20页
This paper presents an optimized strategy for multiple integrations of photovoltaic distributed generation (PV-DG) within radial distribution power systems. The proposed methodology focuses on identifying the optimal ... This paper presents an optimized strategy for multiple integrations of photovoltaic distributed generation (PV-DG) within radial distribution power systems. The proposed methodology focuses on identifying the optimal allocation and sizing of multiple PV-DG units to minimize power losses using a probabilistic PV model and time-series power flow analysis. Addressing the uncertainties in PV output due to weather variability and diurnal cycles is critical. A probabilistic assessment offers a more robust analysis of DG integration’s impact on the grid, potentially leading to more reliable system planning. The presented approach employs a genetic algorithm (GA) and a determined PV output profile and probabilistic PV generation profile based on experimental measurements for one year of solar radiation in Cairo, Egypt. The proposed algorithms are validated using a co-simulation framework that integrates MATLAB and OpenDSS, enabling analysis on a 33-bus test system. This framework can act as a guideline for creating other co-simulation algorithms to enhance computing platforms for contemporary modern distribution systems within smart grids concept. The paper presents comparisons with previous research studies and various interesting findings such as the considered hours for developing the probabilistic model presents different results. 展开更多
关键词 Photovoltaic Distributed Generation Probability Genetic Algorithm Radial Distribution Systems Time Series Power Flow
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Quantum-Enhanced Algorithm for Complex Flow Field Simulation in Near-Space 认领 引用
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作者 CUI Fuxin KANG Jian XU Liang 《Aerospace China》 2025年第3期46-54,共9页
High-fidelity simulation of rarefied gas flows in near-space is essential for hypersonic vehicle design,but the classical Direct Simulation Monte Carlo(DSMC)method faces computational bottlenecks due to its O(N2)co... High-fidelity simulation of rarefied gas flows in near-space is essential for hypersonic vehicle design,but the classical Direct Simulation Monte Carlo(DSMC)method faces computational bottlenecks due to its O(N2)complexity in molecular collision handling.This paper proposes a quantum-enhanced DSMC algorithm that leverages the Grover search algorithm to accelerate collision pair selection,reducing the complexity to O(N).The algorithm encodes molecular pairs into quantum superpositions and applies amplitude amplification for parallel screening,while retaining the full physical fidelity of classical DSMC.Numerical experiments validate the efficient preparation of high-fidelity Gaussian states for representing Maxwell-Boltzmann distributions,and confirm that the quantum-enhanced DSMC maintains physical accuracy while exhibiting increasingly pronounced acceleration with system size.This work offers a novel pathway for efficient simulation of complex near-space flow fields. 展开更多
关键词 quantum computing near‑space flow DSMC Grover algorithm collision selection
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Research on Reservoir Dispatch Optimization Based on Ecological Flow in Hydraulic and Hydroelectric Engineering 认领 引用
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作者 Yunbo Pu 《电气工程与自动化（中英文版）》 2026年第1期1-5,共5页
As global water demand intensifies due to population growth,industrialization,and urbanization,effective management of water resources becomes increasingly crucial.Reservoirs play a key role in addressing human needs ... As global water demand intensifies due to population growth,industrialization,and urbanization,effective management of water resources becomes increasingly crucial.Reservoirs play a key role in addressing human needs such as water supply,power generation,and flood control.However,the traditional approach to reservoir dispatch optimization often neglects the ecological consequences of water allocation,leading to detrimental effects on aquatic ecosystems.This research explores the integration of ecological flow requirements into reservoir dispatch optimization,aiming to achieve a balance between human and environmental needs.Ecological flow,which refers to the water flow necessary to maintain healthy aquatic ecosystems,is essential for the preservation of biodiversity,habitat quality,and overall ecosystem health.This paper discusses the challenges and methods for incorporating ecological flow into reservoir dispatch models,as well as the various optimization techniques used to address these challenges.The findings of this study emphasize the importance of balancing ecological sustainability with human resource needs and highlight the potential of optimization algorithms to effectively address this dual challenge. 展开更多
关键词 Reservoir Dispatch Optimization Ecological Flow Water Resource Management Ecological Sustainability Optimization Algorithms Hydraulic Engineering
A Discrete Artificial Bee Colony Algorithm for Minimizing the Total Flow Time in the Blocking Flow Shop Scheduling 认领 引用 被引量：11
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作者 邓冠龙 徐震浩 顾幸生 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2012年第6期1067-1073,共7页
A discrete artificial bee colony algorithm is proposed for solving the blocking flow shop scheduling problem with total flow time criterion. Firstly, the solution in the algorithm is represented as job permutation. Se... A discrete artificial bee colony algorithm is proposed for solving the blocking flow shop scheduling problem with total flow time criterion. Firstly, the solution in the algorithm is represented as job permutation. Secondly, an initialization scheme based on a variant of the NEH (Nawaz-Enscore-Ham) heuristic and a local search is designed to construct the initial population with both quality and diversity. Thirdly, based on the idea of iterated greedy algorithm, some newly designed schemes for employed bee, onlooker bee and scout bee are presented. The performance of the proposed algorithm is tested on the well-known Taillard benchmark set, and the computational results demonstrate the effectiveness of the discrete artificial bee colony algorithm. In addition, the best known solutions of the benchmark set are provided for the blocking flow shop scheduling problem with total flow time criterion. 展开更多
关键词 blocking flow shop scheduling artificial bee colony algorithm total flow time
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Algorithm for cavity flow in a new-born goaf and experimental verification 认领 引用 被引量：3
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作者 Jian Liu Qichao Zhou +2 位作者 Dong Wang Lijun Deng Ke Gao 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2023年第3期351-361,共11页
Prevention and control measures of spontaneous combustion of coal and gas accumulation in a goaf require an accurate description of its gas flow state.However,the commonly used fluid dynamics in porous media is not su... Prevention and control measures of spontaneous combustion of coal and gas accumulation in a goaf require an accurate description of its gas flow state.However,the commonly used fluid dynamics in porous media is not suitable for the new-born goaf with fracture cavity combination,multi-scale,and large blocks.In this study,we propose a cavity flow algorithm to accurately describe the gas flow state in the new-born goaf.The genetic algorithm(GA)is used to randomly generate the binary matrix of a goaf caving shape.The difference between the gas flow state calculated by the lattice Boltzmann method(LBM)and the measured data at the boundary or internal measuring points of the real goaf is taken as the GA fitness value,and the real goaf caving shape and the gas flow state are quickly addressed by GA.The experimental model of new-born goaf is established,and the laser Doppler anemometry(LDA)experiment is carried out.The results show that the Jaccard similarity coefficient between the reconstructed caving shape and the real caving shape is 0.7473,the mean square error between the calculated wind speed and the LDA-measured value is 0.0244,and the R2 coefficient is 0.8986,which verify the feasibility of the algorithm. 展开更多
关键词 Cavity flow Lattice Boltzmann method Laser Doppler anemometry New-born goaf Genetic algorithm
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Study on polarized optical flow algorithm for imaging bionic polarization navigation micro sensor 认领 引用 被引量：3
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作者 GUAN Le LlU Sheng +3 位作者 LI Shi-qi LIN Wei ZHAI Li-yuan and CHU Jin-kui. 《Optoelectronics Letters》 EI 2018年第3期220-225,共6页
At present,both the point source and the imaging polarization navigation devices only can output the angle information,which means that the velocity information of the carrier cannot be extracted from the polarization... At present,both the point source and the imaging polarization navigation devices only can output the angle information,which means that the velocity information of the carrier cannot be extracted from the polarization field pattern directly.Optical flow is an image-based method for calculating the velocity of pixel point movement in an image.However,for ordinary optical flow,the difference in pixel value as well as the calculation accuracy can be reduced in weak light.Polarization imaging technology has the ability to improve both the detection accuracy and the recognition probability of the target because it can acquire the extra polarization multi-dimensional information of target radiation or reflection.In this paper,combining the polarization imaging technique with the traditional optical flow algorithm,a polarization optical flow algorithm is proposed,and it is verified that the polarized optical flow algorithm has good adaptation in weak light and can improve the application range of polarization navigation sensors.This research lays the foundation for day and night all-weather polarization navigation applications in future. 展开更多
关键词 polarized optical flow algorithm imaging bionic polarization navigation micro sensor
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Planning of a Single Flow Channel in Valve Blocks Based on Additive Manufacturing and the Ant Colony Algorithm 认领 引用 被引量：3
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作者 Jin Zhang Ziyang Li +3 位作者 Yuying Zhang Yandong Liu Ying Li Xiangdong Kong 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2024年第5期191-202,共12页
As electro-hydrostatic actuator(EHA)technology advances towards lightweight and integration,the demand for enhanced internal flow pathways in hydraulic valve blocks intensifies.However,owing to the constraints imposed... As electro-hydrostatic actuator(EHA)technology advances towards lightweight and integration,the demand for enhanced internal flow pathways in hydraulic valve blocks intensifies.However,owing to the constraints imposed by traditional manufacturing processes,conventional hydraulic integrated valve blocks fail to satisfy the demands of a more compact channel layout and lower energy dissipation.Notably,the subjectivity in the arrangement of internal passages results in a time-consuming and labor-intensive process.This study employed additive manufacturing technology and the ant colony algorithm and B-spline curves for the meticulous design of internal passages within an aviation EHA valve block.The layout environment for the valve block passages was established,and path optimization was achieved using the ant colony algorithm,complemented by smoothing using B-spline curves.Three-dimensional modeling was performed using SolidWorks software,revealing a 10.03%reduction in volume for the optimized passages compared with the original passages.Computational fluid dynamics(CFD)simulations were performed using Fluent software,demonstrating that the algorithmically optimized passages effectively prevented the occurrence of vortices at right-angled locations,exhibited superior flow characteristics,and concurrently reduced pressure losses by 34.09%-36.36%.The small discrepancy between the experimental and simulation results validated the efficacy of the ant colony algorithm and B-spline curves in optimizing the passage design,offering a viable solution for channel design in additive manufacturing. 展开更多
关键词 Hydraulic valve block Flow channel B-spline curve Additive manufacturing Ant colony algorithm
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Expressway traffic flow prediction using chaos cloud particle swarm algorithm and PPPR model 认领 引用 被引量：3
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作者 赵泽辉 康海贵 李明伟 《Journal of Southeast University(English Edition)》 EI CAS 2013年第3期328-335,共8页
Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traf... Aiming at the real-time fluctuation and nonlinear characteristics of the expressway short-term traffic flow forecasting the parameter projection pursuit regression PPPR model is applied to forecast the expressway traffic flow where the orthogonal Hermite polynomial is used to fit the ridge functions and the least square method is employed to determine the polynomial weight coefficient c.In order to efficiently optimize the projection direction a and the number M of ridge functions of the PPPR model the chaos cloud particle swarm optimization CCPSO algorithm is applied to optimize the parameters. The CCPSO-PPPR hybrid optimization model for expressway short-term traffic flow forecasting is established in which the CCPSO algorithm is used to optimize the optimal projection direction a in the inner layer while the number M of ridge functions is optimized in the outer layer.Traffic volume weather factors and travel date of the previous several time intervals of the road section are taken as the input influencing factors. Example forecasting and model comparison results indicate that the proposed model can obtain a better forecasting effect and its absolute error is controlled within [-6,6] which can meet the application requirements of expressway traffic flow forecasting. 展开更多
关键词 expressway traffic flow forecasting projectionpursuit regression particle swarm algorithm chaoticmapping cloud model
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Algorithmic approach to discrete fracture network flow modeling in consideration of realistic connections in large-scale fracture networks 认领 引用 被引量：1
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作者 Qihua Zhang Shan Dong +2 位作者 Yaoqi Liu Junjie Huang Feng Xiong 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第9期3798-3811,共14页
Analyzing rock mass seepage using the discrete fracture network(DFN)flow model poses challenges when dealing with complex fracture networks.This paper presents a novel DFN flow model that incorporates the actual conne... Analyzing rock mass seepage using the discrete fracture network(DFN)flow model poses challenges when dealing with complex fracture networks.This paper presents a novel DFN flow model that incorporates the actual connections of large-scale fractures.Notably,this model efficiently manages over 20,000 fractures without necessitating adjustments to the DFN geometry.All geometric analyses,such as identifying connected fractures,dividing the two-dimensional domain into closed loops,triangulating arbitrary loops,and refining triangular elements,are fully automated.The analysis processes are comprehensively introduced,and core algorithms,along with their pseudo-codes,are outlined and explained to assist readers in their programming endeavors.The accuracy of geometric analyses is validated through topological graphs representing the connection relationships between fractures.In practical application,the proposed model is employed to assess the water-sealing effectiveness of an underground storage cavern project.The analysis results indicate that the existing design scheme can effectively prevent the stored oil from leaking in the presence of both dense and sparse fractures.Furthermore,following extensive modification and optimization,the scale and precision of model computation suggest that the proposed model and developed codes can meet the requirements of engineering applications. 展开更多
关键词 Discrete fracture network(DFN)flow model Geometric algorithm Fracture flow Water-sealing effect
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Hybrid evolutionary algorithm for no-wait flow shops to minimize makespan and total flowtime 认领 引用 被引量：2
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作者 廖小平 刘有根 李小平 《Journal of Southeast University(English Edition)》 EI CAS 2008年第4期450-454,共5页
The NP-hard no-wait flow shop scheduling problems with makespan and total flowtime minimization are considered. Objective increment properties of the problems are analyzed. A non-dominated classification method is int... The NP-hard no-wait flow shop scheduling problems with makespan and total flowtime minimization are considered. Objective increment properties of the problems are analyzed. A non-dominated classification method is introduced to class population individuals into Pareto fronts to improve searching efficiency. Besides investigating the crowding distance and the elitist solution strategy, two effective bi-criteria local search procedures based on objective increments are presented to improve searching effectiveness. Based on the properties and methods, a hybrid evolutionary algorithm is proposed for the considered problems and compared with the best existing algorithms. Experimental results show that the proposed algorithm is effective with high efficiency. 展开更多
关键词 no-wait flow shop objective increment makespan total flowtime evolutionary algorithm
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An effective discrete artificial bee colony algorithm for flow shop scheduling problem with intermediate buffers 认领 引用 被引量：6
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作者 张素君 顾幸生 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第9期3471-3484,共14页
An effective discrete artificial bee colony(DABC) algorithm is proposed for the flow shop scheduling problem with intermediate buffers(IBFSP) in order to minimize the maximum completion time(i.e makespan). The effecti... An effective discrete artificial bee colony(DABC) algorithm is proposed for the flow shop scheduling problem with intermediate buffers(IBFSP) in order to minimize the maximum completion time(i.e makespan). The effective combination of the insertion and swap operator is applied to producing neighborhood individual at the employed bee phase. The tournament selection is adopted to avoid falling into local optima, while, the optimized insert operator embeds in onlooker bee phase for further searching the neighborhood solution to enhance the local search ability of algorithm. The tournament selection with size 2 is again applied and a better selected solution will be performed destruction and construction of iterated greedy(IG) algorithm, and then the result replaces the worse one. Simulation results show that our algorithm has a better performance compared with the HDDE and CHS which were proposed recently. It provides the better known solutions for the makespan criterion to flow shop scheduling problem with limited buffers for the Car benchmark by Carlier and Rec benchmark by Reeves. The convergence curves show that the algorithm not only has faster convergence speed but also has better convergence value. 展开更多
关键词 discrete artificial bee colony algorithm flow shop scheduling problem with intermediate buffers destruction and construction tournament selection
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Convergence and stability of the Newton-Like algorithm with estimation error in optimization flow control 认领 引用 被引量：1
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作者 Yang Jun Li Shiyong +1 位作者 Long Chengnian Guan Xinping 《Journal of Systems Engineering and Electronics》 SCIE EI 2007年第3期591-597,共7页
The Newton-Like algorithm with price estimation error in optimization flow control in network is analyzed. The estimation error is treated as inexactness of the gradient and the inexact descent direction is analyzed. ... The Newton-Like algorithm with price estimation error in optimization flow control in network is analyzed. The estimation error is treated as inexactness of the gradient and the inexact descent direction is analyzed. Based on the optimization theory, a sufficient condition for convergence of this algorithm with bounded price estimation error is obtained. Furthermore, even when this sufficient condition doesn＇t hold, this algorithm can also converge, provided a modified step size, and an attraction region is obtained. Based on Lasalle＇s invariance principle applied to a suitable Lyapunov function, the dynamic system described by this algorithm is proved to be global stability if the error is zero. And the Newton-Like algorithm with bounded price estimation error is also globally stable if the error satisfies the sufficient condition for convergence. All trajectories ultimately converge to the equilibrium point. 展开更多
关键词 flow control Newton-Like algorithm convergence global stability optimization Lyapunov function.
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Improved Social Emotion Optimization Algorithm for Short-Term Traffic Flow Forecasting Based on Back-Propagation Neural Network 认领 引用 被引量：3
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作者 ZHANG Jun ZHAO Shenwei +1 位作者 WANG Yuanqiang ZHU Xinshan 《Journal of Shanghai Jiaotong university(Science)》 EI 2019年第2期209-219,共11页
The back-propagation neural network(BPNN) is a well-known multi-layer feed-forward neural network which is trained by the error reverse propagation algorithm. It is very suitable for the complex of short-term traffic ... The back-propagation neural network(BPNN) is a well-known multi-layer feed-forward neural network which is trained by the error reverse propagation algorithm. It is very suitable for the complex of short-term traffic flow forecasting; however, BPNN is easy to fall into local optimum and slow convergence. In order to overcome these deficiencies, a new approach called social emotion optimization algorithm(SEOA) is proposed in this paper to optimize the linked weights and thresholds of BPNN. Each individual in SEOA represents a BPNN. The availability of the proposed forecasting models is proved with the actual traffic flow data of the 2 nd Ring Road of Beijing. Experiment of results show that the forecasting accuracy of SEOA is improved obviously as compared with the accuracy of particle swarm optimization back-propagation(PSOBP) and simulated annealing particle swarm optimization back-propagation(SAPSOBP) models. Furthermore, since SEOA does not respond to the negative feedback information, Metropolis rule is proposed to give consideration to both positive and negative feedback information and diversify the adjustment methods. The modified BPNN model, in comparison with social emotion optimization back-propagation(SEOBP) model, is more advantageous to search the global optimal solution. The accuracy of Metropolis rule social emotion optimization back-propagation(MRSEOBP) model is improved about 19.54% as compared with that of SEOBP model in predicting the dramatically changing data. 展开更多
关键词 urban traffic short-term traffic flow forecasting social emotion optimization algorithm(SEOA) back-propagation neural network(BPNN) Metropolis rule
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A novel hybrid estimation of distribution algorithm for solving hybrid flowshop scheduling problem with unrelated parallel machine 认领 引用 被引量：9
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作者 孙泽文 顾幸生 《Journal of Central South University》 SCIE EI CAS CSCD 2017年第8期1779-1788,共10页
The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this wor... The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms. 展开更多
关键词 hybrid estimation of distribution algorithm teaching learning based optimization strategy hybrid flow shop unrelated parallel machine scheduling
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