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
The reasonable measuring of particle weight and effective sampling of particle state are consid- ered as two important aspects to obtain better estimation precision in particle filter. Aiming at the comprehensive trea...The reasonable measuring of particle weight and effective sampling of particle state are consid- ered as two important aspects to obtain better estimation precision in particle filter. Aiming at the comprehensive treatment of above problems, a novel two-stage prediction and update particle filte- ring algorithm based on particle weight optimization in multi-sensor observation is proposed. Firstly, combined with the construction of muhi-senor observation likelihood function and the weight fusion principle, a new particle weight optimization strategy in multi-sensor observation is presented, and the reliability and stability of particle weight are improved by decreasing weight variance. In addi- tion, according to the prediction and update mechanism of particle filter and unscented Kalman fil- ter, a new realization of particle filter with two-stage prediction and update is given. The filter gain containing the latest observation information is used to directly optimize state estimation in the frame- work, which avoids a large calculation amount and the lack of universality in proposal distribution optimization way. The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.展开更多
To decrease the impact of shorter product life cycles,dynamic cell formation problems(CFPs)and cell layout problems(CLPs)were simultaneously optimized.First,CFPs and CLPs were formally described.Due to the changes of ...To decrease the impact of shorter product life cycles,dynamic cell formation problems(CFPs)and cell layout problems(CLPs)were simultaneously optimized.First,CFPs and CLPs were formally described.Due to the changes of product demands and the lim it of machine capacity,the existing layout needed to be rearranged to a high degree.Secondly,a mathematical model was established for the objective function of minimizing the total costs.Thirdly,a novel dynamic multi-swarm particle swarm optimization(DMS-PSO)algorithm based on the communication learning strategy(CLS)was developed.Toavoid falling into local optimum and slow convergence,each swarm shared their optimal locations before regrouping.Finally,simulation experiments were conducted under different conditions.Numerical results indicate that the proposed algorithm has better stability and it converges faster than other existing algorithms.展开更多
This paper deals with the solution concepts,scalarization and existence of solutions formultiobjective generalized game. The scalarization method used in this paper can characterizecompletely the solutions and be appl...This paper deals with the solution concepts,scalarization and existence of solutions formultiobjective generalized game. The scalarization method used in this paper can characterizecompletely the solutions and be applied to prove the existence of solutions for quasi-convexmultiobjective generalized game. On the other hand,a new concept of security strategy isintroduced and its existence is proved.At last,some relations between these solutions areestablished.展开更多
文摘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.
基金Supported by the National Natural Science Foundations of China(No.61300214,61170243)the Science and Technology Innovation Team Support Plan of Education Department of Henan Province(No.13IRTSTHN021)+2 种基金the Science and Technology Research Key Project of Education Department of Henan Province(No.13A413066)the Basic and Frontier Technology Research Plan of Henan Province(No.132300410148)the Funding Scheme of Young Key Teacher of Henan Province Universities,and the Key Project of Teaching Reform Research of Henan University(No.HDXJJG2013-07)
文摘The reasonable measuring of particle weight and effective sampling of particle state are consid- ered as two important aspects to obtain better estimation precision in particle filter. Aiming at the comprehensive treatment of above problems, a novel two-stage prediction and update particle filte- ring algorithm based on particle weight optimization in multi-sensor observation is proposed. Firstly, combined with the construction of muhi-senor observation likelihood function and the weight fusion principle, a new particle weight optimization strategy in multi-sensor observation is presented, and the reliability and stability of particle weight are improved by decreasing weight variance. In addi- tion, according to the prediction and update mechanism of particle filter and unscented Kalman fil- ter, a new realization of particle filter with two-stage prediction and update is given. The filter gain containing the latest observation information is used to directly optimize state estimation in the frame- work, which avoids a large calculation amount and the lack of universality in proposal distribution optimization way. The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.
基金The National Natural Science Foundation of China(No.71471135)
文摘To decrease the impact of shorter product life cycles,dynamic cell formation problems(CFPs)and cell layout problems(CLPs)were simultaneously optimized.First,CFPs and CLPs were formally described.Due to the changes of product demands and the lim it of machine capacity,the existing layout needed to be rearranged to a high degree.Secondly,a mathematical model was established for the objective function of minimizing the total costs.Thirdly,a novel dynamic multi-swarm particle swarm optimization(DMS-PSO)algorithm based on the communication learning strategy(CLS)was developed.Toavoid falling into local optimum and slow convergence,each swarm shared their optimal locations before regrouping.Finally,simulation experiments were conducted under different conditions.Numerical results indicate that the proposed algorithm has better stability and it converges faster than other existing algorithms.
文摘This paper deals with the solution concepts,scalarization and existence of solutions formultiobjective generalized game. The scalarization method used in this paper can characterizecompletely the solutions and be applied to prove the existence of solutions for quasi-convexmultiobjective generalized game. On the other hand,a new concept of security strategy isintroduced and its existence is proved.At last,some relations between these solutions areestablished.
