In this paper,we propose a variable metric method for unconstrained multiobjective optimization problems(MOPs).First,a sequence of points is generated using different positive definite matrices in the generic framewor...In this paper,we propose a variable metric method for unconstrained multiobjective optimization problems(MOPs).First,a sequence of points is generated using different positive definite matrices in the generic framework.It is proved that accumulation points of the sequence are Pareto critical points.Then,without convexity assumption,strong convergence is established for the proposed method.Moreover,we use a common matrix to approximate the Hessian matrices of all objective functions,along which a new nonmonotone line search technique is proposed to achieve a local superlinear convergence rate.Finally,several numerical results demonstrate the effectiveness of the proposed method.展开更多
This paper explores the convergence of a class of optimally conditioned self scaling variable metric (OCSSVM) methods for unconstrained optimization. We show that this class of methods with Wolfe line search are glob...This paper explores the convergence of a class of optimally conditioned self scaling variable metric (OCSSVM) methods for unconstrained optimization. We show that this class of methods with Wolfe line search are globally convergent for general convex functions.展开更多
A new method for computing numerical solutions to the inverse kinematics problem of robotic manipulators is developed in this paper.With the joint limitations,the electromagnetism-like method(EM)utilizes an attraction...A new method for computing numerical solutions to the inverse kinematics problem of robotic manipulators is developed in this paper.With the joint limitations,the electromagnetism-like method(EM)utilizes an attraction-repulsion mechanism to move the sample points towards the optimum solution rapidly.Based on this approximate solution given by EM,a modified DavidonFletcher-Powell(DFP)algorithm is developed to solve the problem at the desired precision.Unlike the traditional algorithms,this modified DFP(MDFP)algorithm randomly chooses the search step size between 0 and 1.Hence,the computational complexity is greatly reduced.The experimental results based on ten general test functions and PUMA 560 robot show that this new near-real time hybrid method can produce best performance.展开更多
基金the Major Program of the National Natural Science Foundation of China(Nos.11991020 and 11991024)the National Natural Science Foundation of China(Nos.11971084 and 12171060)+1 种基金the Natural Science Foundation of Chongqing(No.cstc2019jcyj-zdxmX0016)Foundation of Chongqing Normal University(Nos.22XLB005 and 22XLB006).
文摘In this paper,we propose a variable metric method for unconstrained multiobjective optimization problems(MOPs).First,a sequence of points is generated using different positive definite matrices in the generic framework.It is proved that accumulation points of the sequence are Pareto critical points.Then,without convexity assumption,strong convergence is established for the proposed method.Moreover,we use a common matrix to approximate the Hessian matrices of all objective functions,along which a new nonmonotone line search technique is proposed to achieve a local superlinear convergence rate.Finally,several numerical results demonstrate the effectiveness of the proposed method.
文摘This paper explores the convergence of a class of optimally conditioned self scaling variable metric (OCSSVM) methods for unconstrained optimization. We show that this class of methods with Wolfe line search are globally convergent for general convex functions.
基金Supported by National High Technology Research and Development Program of China(863 Program)(2008AA04Z214)National Natural Science Foundation of China(2008BAF36B01)
文摘A new method for computing numerical solutions to the inverse kinematics problem of robotic manipulators is developed in this paper.With the joint limitations,the electromagnetism-like method(EM)utilizes an attraction-repulsion mechanism to move the sample points towards the optimum solution rapidly.Based on this approximate solution given by EM,a modified DavidonFletcher-Powell(DFP)algorithm is developed to solve the problem at the desired precision.Unlike the traditional algorithms,this modified DFP(MDFP)algorithm randomly chooses the search step size between 0 and 1.Hence,the computational complexity is greatly reduced.The experimental results based on ten general test functions and PUMA 560 robot show that this new near-real time hybrid method can produce best performance.
