13 results on '"Jiao, Hongwei"'
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2. Globally fuzzy consensus of hybrid-order stochastic nonlinear multi-agent systems.
- Author
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Chen, Jiaxi, Li, Junmin, Jiao, Hongwei, and Zhang, Shuai
- Subjects
MULTIAGENT systems ,NONLINEAR systems ,STABILITY theory ,LYAPUNOV stability ,LYAPUNOV functions ,LOCAL mass media - Abstract
This paper studies the globally fuzzy consensus of stochastic nonlinear multi-agent systems (MAS) with hybrid-order dynamics. The followers are modeled as hybrid first- and second-order systems. The leader is presented as second-order system and can transmit his own states to the first- and second-order followers. In view of the local characteristics of communication among agents, the followers can be decomposed into two categories: one is the set of followers who can communicate with the leader, and the other is the set of followers who cannot communicate with the leader. Using the design method of fuzzy feed-forward compensator and Lyapunov stability theory, a new hybrid fuzzy consensus controller is designed for the two kinds of follower sets. Compared with most stochastic MAS, the proposed algorithm not only solves the consensus of hybrid-order stochastic MAS based on fuzzy approximator, but also obtains the results of globally uniform ultimate bounded (GUUD). In the end, the simulation results further verify the validity of the proposed algorithm. • Distributed control problems of stochastic mixed-order multi-agent systems are solved. • A new class of Lyapunov functions is constructed to reduce the conservatism of algorithm design. • Globally fuzzy consensus results are obtained. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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3. A nonisolated optimal solution of general linear multiplicative programming problems
- Author
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Chen, Yongqiang and Jiao, Hongwei
- Subjects
Algorithm ,Algorithms - Abstract
To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cor.2008.11.002 Byline: Yongqiang Chen (a), Hongwei Jiao (b) Abstract: This article presents a finite branch-and-bound algorithm for globally solving general linear multiplicative programming problems (GLMP). The proposed algorithm is based on the recently developed theory of monotonic optimization. The proposed algorithm provides a nonisolated global optimal solution, and it turns out that such an optimal solution is adequately guaranteed to be feasible and to be close to the actual optimal solution. It can be shown by the numerical results that the proposed algorithm is effective and the computational results can be gained in short time. Author Affiliation: (a) College of Mathematics and Information Science, Henan Normal University, Xinxiang 453007, PR China (b) Department of Mathematics, Henan Institute of Science and Technology, Xinxiang 453003, PR China Article Note: (footnote) [star] Research supported by the National Natural Science Foundation of China under Grant 10671057.
- Published
- 2009
4. An efficient algorithm and complexity result for solving the sum of general affine ratios problem.
- Author
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Jiao, Hongwei and Ma, Junqiao
- Subjects
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OUTER space , *RUNNING speed , *ALGORITHMS , *TRANSPORTATION planning , *LINEAR programming , *RECTANGLES - Abstract
In this paper, with the unique hypothesis that the denominator is not equal to 0, an efficient outer space rectangle branch-and-bound algorithm is presented to globally solve the sum of general affine ratios problem. By using equivalent transformation and the characteristics of general single ratio function, a linear program relaxation problem is constructed for computing the lower bound of the global minimum value of the original problem. Moreover, to enhance the running speed of the presented algorithm, we design an outer space accelerating technic for deleting the entire outer space rectangle or a part of the entire examined outer space rectangle, in which there contains no the global minimum point of the equivalent problem. Furthermore, through the complexity analysis of the presented algorithm, we estimate its maximum iteration times. In addition, we prove the global convergence of the presented algorithm, report and analyze the numerical computational results for indicating the validity of the presented algorithm. Finally, two practical problems from power transportation and production planning are solved to verify the usefulness of the presented algorithm. • We present a novel outer space branch-and-bound algorithm for the SGAR. • The algorithm economizes the required computations by dividing space. • The main calculation involves solving a series of linear programming problems. • We give the complexity analysis of the algorithm for the first time. • The computing efficiency is significantly higher than the existing algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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5. Solving min–max linear fractional programs based on image space branch-and-bound scheme.
- Author
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Jiao, Hongwei and Li, Binbin
- Subjects
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COMPUTATIONAL complexity , *INDUSTRIAL efficiency , *FRACTIONAL programming , *GLOBAL optimization , *ALGORITHMS , *RECTANGLES , *SEMIDEFINITE programming - Abstract
Based on the image space branch-and-bound scheme, this paper presents a novel algorithm for globally solving the min–max linear fractional programs (MMLFP), which has many applications in management optimization, engineering optimization, economic investment and so on. For finding the global optimal solution of the MMLFP, by leading into new parameter, we firstly transform the MMLFP into an equivalent fractional problem (EFP). Next, by using convex hull and concave hull approximation of bilinear function, we construct the linear relaxation problem (LRP) for computing the lower bound of the global minimum value of the EFP in the image space branch-and-bound algorithm. By subsequently solving a series of linear relaxation problems and refining the initial image space rectangle, the proposed algorithm is globally convergent to the optimum solution of the EFP. In addition, we give the computational complexity analysis of the algorithm based on the exhaustive branching rule. Finally, computational comparisons show better computational performance of the algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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6. A parametric linear relaxation algorithm for globally solving nonconvex quadratic programming.
- Author
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Jiao, Hongwei, Liu, Sanyang, and Lu, Nan
- Subjects
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LINEAR systems , *ALGORITHMS , *QUADRATIC programming , *PROBLEM solving , *MATHEMATICAL optimization - Abstract
In this article, we present a parametric linear relaxation algorithm for globally solving the nonconvex quadratic programming (NQP). In this algorithm, a new parametric linearized technique is proposed for generating parametric linear relaxation programming (PLRP) of the NQP, which can be used to determine the lower bound of global minimum value of the NQP. To improve the convergent speed of the proposed algorithm, a pruning operation is employed to compress the investigated region. By subdividing subsequently the initial domain and solving subsequently a series of parametric linear relaxation programming problems over the subdivided domain, the proposed algorithm is convergent to the global minimum of the NQP. Finally, an engineering problem for the design of heat exchanger network and some test examples are used to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2015
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7. Global optimization algorithm for sum of generalized polynomial ratios problem
- Author
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Jiao, Hongwei, Wang, Zhankui, and Chen, Yongqiang
- Subjects
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GLOBAL analysis (Mathematics) , *MATHEMATICAL optimization , *POLYNOMIALS , *LINEAR programming , *ALGORITHMS , *MATHEMATICAL transformations , *EXPONENTS , *NONCONVEX programming - Abstract
Abstract: In this paper, a global optimization algorithm is proposed for solving sum of generalized polynomial ratios problem (P) which arises in various practical problems. Due to its intrinsic difficulty, less work has been devoted to globally solve the problem (P). For such problems, we present a branch and bound algorithm. In this method, by utilizing exponent transformation and new three-level linear relaxation method, a sequence of linear relaxation programming of the initial nonconvex programming problem (P) are derived which are embedded in a branch and bound algorithm. The proposed method need not introduce new variables and constraints and it is convergent to the global minimum of prime problem by means of the subsequent solutions of a series of linear programming problems. Several numerical examples in the literatures are tested to demonstrate that the proposed algorithm can systematically solve these examples to find the approximate ϵ-global optimum. [Copyright &y& Elsevier]
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- 2013
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8. A branch and bound algorithm for globally solving a class of nonconvex programming problems
- Author
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Jiao, Hongwei
- Subjects
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NONCONVEX programming , *BOUNDARY value problems , *BRANCH & bound algorithms , *LINEAR programming , *MATHEMATICAL optimization , *STOCHASTIC convergence - Abstract
Abstract: A branch and bound algorithm is proposed for globally solving a class of nonconvex programming problems (NP). For minimizing the problem, linear lower bounding functions (LLBFs) of objective function and constraint functions are constructed, then a relaxation linear programming is obtained which is solved by the simplex method and which provides the lower bound of the optimal value. The proposed algorithm is convergent to the global minimum through the successive refinement of linear relaxation of the feasible region and the solutions of a series of linear programming problems. And finally the numerical experiment is reported to show the feasibility and effectiveness of the proposed algorithm. [Copyright &y& Elsevier]
- Published
- 2009
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9. A note on a deterministic global optimization algorithm
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Jiao, Hongwei and Chen, Yongqiang
- Subjects
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DETERMINISTIC chaos , *MATHEMATICAL optimization , *ALGORITHMS , *MATHEMATICS , *LINEAR programming - Abstract
Abstract: In this technical note, we give a short extension application for a deterministic global optimization algorithm proposed in [Y. Ji, K.-C. Zhang, S.-J. Qu, A deterministic global optimization algorithm, Applied Mathematics and Computation 185 (2007) 382–387]. Actually our result is slightly more general, since it does not impose any special sign restrictions on numerators and denominators of the ratios. The only assumption is that the denominators of the ratios are nonzero over the feasible region of the problem. For clarity we use the same notations for the different equivalent problems and the corresponding relaxation linear programming (RLP) as done in Ji et al. (2007). [Copyright &y& Elsevier]
- Published
- 2008
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10. A note on the paper global optimization of nonlinear sum of ratios
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Jiao, Hongwei and Shen, Peiping
- Subjects
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MATHEMATICAL programming , *DIFFERENTIAL equations , *MATHEMATICAL analysis , *COMPLEX numbers - Abstract
Abstract: In this technical note, we give a short extension application for nonlinear sum of ratios problem (P) considered in [Y.J. Wang, K.C. Zhang, Global optimization of nonlinear sum of ratios problem, Applied Mathematics and Computation 158 (2004) 319–330]. Actually our result is slightly more general, since we do not specify additional positive coefficient for each ratio. In this note, we use different equivalent problem as done in Wang and Zhang (2004). Our method introduce p variables less than other method (Wang and Zhang, 2004), and our approach need not additional special program to obtain the upper and lower bound of numerator and denominator for each ratio in the objective function. [Copyright &y& Elsevier]
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- 2007
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11. A new rectangle branch-and-pruning approach for generalized geometric programming
- Author
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Shen, Peiping and Jiao, Hongwei
- Subjects
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MATHEMATICAL programming , *ALGORITHMS , *MATHEMATICAL optimization , *STOCHASTIC convergence - Abstract
Abstract: Generalized geometric programming (GGP) problem occurs frequently in engineering design and management. In this paper, a branch-and-pruning global optimization algorithm is proposed for GGP. By utilizing some transformations, a linear relaxation of the problem (GGP) is obtained based on the linear lower bound functions of objective and constraint functions inside some hyperrectangle region. Then a new pruning technique is given to accelerate the convergence of the given algorithm, and this pruning technique offers the possibility to cut away a large part of the current investigated region in which there no exist global optimum solution. The proposed algorithm which connects branch-and-bound method with the pruning technique successfully is convergent to the global minimum, according to the successive refinement of the linear relaxation of feasible region of the objective function and the solutions of a series of linear relaxation problems. And finally numerical experiment is given to illustrate the feasibility and efficiency of the proposed algorithm. [Copyright &y& Elsevier]
- Published
- 2006
- Full Text
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12. Global optimization of generalized linear fractional programming with nonlinear constraints
- Author
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Jiao, Hongwei, Guo, Yunrui, and Shen, Peiping
- Subjects
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ALGORITHMS , *ALGEBRA , *COMPUTER programming , *FEASIBILITY studies - Abstract
Abstract: This paper presents a branch-and-bound algorithm for globally solving a wide class of generalized linear fractional programming problems (GLFP). This class includes such problems as: minimizing a sum, or error for product of a finite number of ratios of linear functions, linear multiplicative programming, polynomial programming, etc. – over nonconvex feasible region. First a problem (Q) is derived which is equivalent to problem (GLFP). In the algorithm, lower bounds are derived by solving a sequence of linear relaxation programming problems, which is based on the construction of the linear lower bounding functions for the objective function and constraint functions of problem (Q) over the feasible region. Convergent property of the presented algorithm is proved and numerical results are given to show the feasibility of the proposed algorithm. [Copyright &y& Elsevier]
- Published
- 2006
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13. Linearization method for a class of multiplicative programming with exponent
- Author
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Shen, Peiping and Jiao, Hongwei
- Subjects
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ALGORITHMS , *MATHEMATICAL programming , *DYNAMIC programming , *LINEAR programming - Abstract
Abstract: This paper presents a global optimization algorithm for a class of multiplicative programming with exponent under multiplicative constraints (MPE). By utilizing equivalent problem of MPE in the sense that they have the same optimal solution, tangential hypersurfaces and concave envelope approximations a linear relaxation of equivalent problem is received. Thus the initial nonconvex programming problem (MPE) is reduced to a sequence of linear programming problems through the successive refinement of a linear relaxation of feasible region of the objective function. The proposed algorithm is convergent to the globally optimal solution of MPE by means of the subsequent solutions of a series of linear programming problems. Numerical results indicate that the proposed algorithm is extremely robust and can be used successfully to solve global minimum of MPE on microcomputer. [Copyright &y& Elsevier]
- Published
- 2006
- Full Text
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