In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objectiv...In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objective FJSP, the Grantt graph oriented string representation （GOSR） and the basic manipulation of the genetic algorithm operator are presented. An integrated operator genetic algorithm （IOGA） and its process are described. Comparison between computational results and the latest research shows that the proposed algorithm is effective in reducing the total workload of all machines, the makespan and the critical machine workload.展开更多
The constriction factor method (CFM) is a new variation of the basic particle swarm optimization (PSO), which has relatively better convergent nature. The effects of the major parameters on CFM were systematically inv...The constriction factor method (CFM) is a new variation of the basic particle swarm optimization (PSO), which has relatively better convergent nature. The effects of the major parameters on CFM were systematically investigated based on some benchmark functions. The constriction factor, velocity constraint, and population size all have significant impact on the per- formance of CFM for PSO. The constriction factor and velocity constraint have optimal values in practical application, and im- proper choice of these factors will lead to bad results. Increasing population size can improve the solution quality, although the computing time will be longer. The characteristics of CFM parameters are described and guidelines for determining parameter values are given in this paper.展开更多
A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrai...A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Com- parison with Multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.展开更多
The control problem of trajectory based path following for passenger vehicles is studied. Comprehensive nonlinear vehicle model is utilized for simulation vehicle response during various maneuvers in MATLAB/Simulink. ...The control problem of trajectory based path following for passenger vehicles is studied. Comprehensive nonlinear vehicle model is utilized for simulation vehicle response during various maneuvers in MATLAB/Simulink. In order to follow desired path, a driver model is developed to enhance closed loop driver/vehicle model. Then, linear quadratic regulator(LQR) controller is developed which regulates direct yaw moment and corrective steering angle on wheels. Particle swam optimization(PSO) method is utilized to optimize the LQR controller for various dynamic conditions. Simulation results indicate that, over various maneuvers, side slip angle and lateral acceleration can be reduced by 10% and 15%, respectively, which sustain the vehicle stable. Also, anti-lock brake system is designed for longitudinal dynamics of vehicle to achieve desired slip during braking and accelerating. Proposed comprehensive controller demonstrates that vehicle steerability can increase by about 15% during severe braking by preventing wheel from locking and reducing stopping distance.展开更多
An approach combining optimal antenna subset selection with blind detection scheme for Orthogonal Space-Time Block Coding (OSTBC) is proposed in this paper. The optimal antenna sub- set selection is taken into account...An approach combining optimal antenna subset selection with blind detection scheme for Orthogonal Space-Time Block Coding (OSTBC) is proposed in this paper. The optimal antenna sub- set selection is taken into account at transmitter and/or receiver sides, which chooses the optimal an- tennas to increase the diversity order of OSTBC and improve further its performance. In order to en- hance the robustness of the detection used in the conventional OSTBC scheme, a blind detection scheme based on Independent Component Analysis (ICA) is exploited which can directly extract transmitted signals without channel estimation. Performance analysis shows that the proposed ap- proach can achieve the full diversity and the flexibility of system design by using the antenna selec-tion and the ICA based blind detection schemes.展开更多
Particle swarm optimization algorithm is presented for the layout of ＂Integrate Circuit （IC）＂ design. Particle swarm optimization based on swarm intelligence is a new evolutionary computational tool and is success...Particle swarm optimization algorithm is presented for the layout of ＂Integrate Circuit （IC）＂ design. Particle swarm optimization based on swarm intelligence is a new evolutionary computational tool and is successfully applied in function optimization, neural network design, classification, pattern recognition, signal processing and robot technology and so on. A modified algorithm is presented and applied to the layout of IC design. For a given layout plane, first of all, this algorithm generates the corresponding grid group by barriers and nets＇ ports with the thought ofgridless net routing, establishes initialization fuzzy matrix, then utilizes the global optimization character to find out the best layout route only if it exits. The results of model simulation indicate that PSO algorithm is feasible and efficient in IC layout design.展开更多
Abstract Accurate forecast of future container throughput of a port is very important for its con struction, upgrading, and operation management. This study proposes a transfer forecasting model guided by discrete par...Abstract Accurate forecast of future container throughput of a port is very important for its con struction, upgrading, and operation management. This study proposes a transfer forecasting model guided by discrete particle swarm optimization algorithm （TF-DPSO）. It firstly transfers some related time series in source domain to assist in modeling the target time series by transfer learning technique, and then constructs the forecasting model by a pattern matching method called analog complexing. Finally, the discrete particle swarm optimization algorithm is introduced to find the optimal match between the two important parameters in TF-DPSO. The container throughput time series of two im portant ports in China, Shanghai Port and Ningbo Port are used for empirical analysis, and the results show the effectiveness of the proposed model.展开更多
文摘In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objective FJSP, the Grantt graph oriented string representation （GOSR） and the basic manipulation of the genetic algorithm operator are presented. An integrated operator genetic algorithm （IOGA） and its process are described. Comparison between computational results and the latest research shows that the proposed algorithm is effective in reducing the total workload of all machines, the makespan and the critical machine workload.
基金Project (No. 20276063) supported by the National Natural Sci-ence Foundation of China
文摘The constriction factor method (CFM) is a new variation of the basic particle swarm optimization (PSO), which has relatively better convergent nature. The effects of the major parameters on CFM were systematically investigated based on some benchmark functions. The constriction factor, velocity constraint, and population size all have significant impact on the per- formance of CFM for PSO. The constriction factor and velocity constraint have optimal values in practical application, and im- proper choice of these factors will lead to bad results. Increasing population size can improve the solution quality, although the computing time will be longer. The characteristics of CFM parameters are described and guidelines for determining parameter values are given in this paper.
基金Project (Nos. 60074040 and 6022506) supported by the NationalNatural Science Foundation of China
文摘A multi-objective particle swarm optimization (MOPSO) approach for multi-objective economic load dispatch problem in power system is presented in this paper. The economic load dispatch problem is a non-linear constrained multi-objective optimization problem. The proposed MOPSO approach handles the problem as a multi-objective problem with competing and non-commensurable fuel cost, emission and system loss objectives and has a diversity-preserving mechanism using an external memory (call “repository”) and a geographically-based approach to find widely different Pareto-optimal solutions. In addition, fuzzy set theory is employed to extract the best compromise solution. Several optimization runs of the proposed MOPSO approach were carried out on the standard IEEE 30-bus test system. The results revealed the capabilities of the proposed MOPSO approach to generate well-distributed Pareto-optimal non-dominated solutions of multi-objective economic load dispatch. Com- parison with Multi-objective Evolutionary Algorithm (MOEA) showed the superiority of the proposed MOPSO approach and confirmed its potential for solving multi-objective economic load dispatch.
文摘The control problem of trajectory based path following for passenger vehicles is studied. Comprehensive nonlinear vehicle model is utilized for simulation vehicle response during various maneuvers in MATLAB/Simulink. In order to follow desired path, a driver model is developed to enhance closed loop driver/vehicle model. Then, linear quadratic regulator(LQR) controller is developed which regulates direct yaw moment and corrective steering angle on wheels. Particle swam optimization(PSO) method is utilized to optimize the LQR controller for various dynamic conditions. Simulation results indicate that, over various maneuvers, side slip angle and lateral acceleration can be reduced by 10% and 15%, respectively, which sustain the vehicle stable. Also, anti-lock brake system is designed for longitudinal dynamics of vehicle to achieve desired slip during braking and accelerating. Proposed comprehensive controller demonstrates that vehicle steerability can increase by about 15% during severe braking by preventing wheel from locking and reducing stopping distance.
基金Partially supported by National Natural Science Foun-dation of China (60572105)Open Foundations of the State Key Laboratory of Mobile Communications (A200508)+1 种基金the State Key Lab of Integrated Services Networks (ISN7-02)the Program for New Century Excellent Talents (NCET-05-0582) in University.
文摘An approach combining optimal antenna subset selection with blind detection scheme for Orthogonal Space-Time Block Coding (OSTBC) is proposed in this paper. The optimal antenna sub- set selection is taken into account at transmitter and/or receiver sides, which chooses the optimal an- tennas to increase the diversity order of OSTBC and improve further its performance. In order to en- hance the robustness of the detection used in the conventional OSTBC scheme, a blind detection scheme based on Independent Component Analysis (ICA) is exploited which can directly extract transmitted signals without channel estimation. Performance analysis shows that the proposed ap- proach can achieve the full diversity and the flexibility of system design by using the antenna selec-tion and the ICA based blind detection schemes.
文摘Particle swarm optimization algorithm is presented for the layout of ＂Integrate Circuit （IC）＂ design. Particle swarm optimization based on swarm intelligence is a new evolutionary computational tool and is successfully applied in function optimization, neural network design, classification, pattern recognition, signal processing and robot technology and so on. A modified algorithm is presented and applied to the layout of IC design. For a given layout plane, first of all, this algorithm generates the corresponding grid group by barriers and nets＇ ports with the thought ofgridless net routing, establishes initialization fuzzy matrix, then utilizes the global optimization character to find out the best layout route only if it exits. The results of model simulation indicate that PSO algorithm is feasible and efficient in IC layout design.
基金partly supported by the Natural Science Foundation of China under Grant Nos.71101100 and 70731160635New Teachers’Fund for Doctor Stations,Ministry of Education under Grant No.20110181120047+5 种基金Excellent Youth Fund of Sichuan University under Grant No.2013SCU04A08China Postdoctoral Science Foundation under Grant Nos.2011M500418,2012T50148 and 2013M530753Frontier and Cross-innovation Foundation of Sichuan University under Grant No.skqy201352Soft Science Foundation of Sichuan Province under Grant No.2013ZR0016Humanities and Social Sciences Youth Foundation of the Ministry of Education of China under Grant No.11YJC870028Selfdetermined Research Funds of CCNU from the Colleges’ Basic Research and Operation of MOE under Grant No.CCNU13F030
文摘Abstract Accurate forecast of future container throughput of a port is very important for its con struction, upgrading, and operation management. This study proposes a transfer forecasting model guided by discrete particle swarm optimization algorithm （TF-DPSO）. It firstly transfers some related time series in source domain to assist in modeling the target time series by transfer learning technique, and then constructs the forecasting model by a pattern matching method called analog complexing. Finally, the discrete particle swarm optimization algorithm is introduced to find the optimal match between the two important parameters in TF-DPSO. The container throughput time series of two im portant ports in China, Shanghai Port and Ningbo Port are used for empirical analysis, and the results show the effectiveness of the proposed model.
