• Title/Summary/Keyword: Constrained nonlinear optimization problem

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Hopfield neuron based nonlinear constrained programming to fuzzy structural engineering optimization

  • Shih, C.J.;Chang, C.C.
    • Structural Engineering and Mechanics
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    • v.7 no.5
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    • pp.485-502
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    • 1999
  • Using the continuous Hopfield network model as the basis to solve the general crisp and fuzzy constrained optimization problem is presented and examined. The model lies in its transformation to a parallel algorithm which distributes the work of numerical optimization to several simultaneously computing processors. The method is applied to different structural engineering design problems that demonstrate this usefulness, satisfaction or potential. The computing algorithm has been given and discussed for a designer who can program it without difficulty.

An Efficient Method for Nonlinear Optimization Problems using Genetic Algorithms (유전해법을 이용한 비선형최적화 문제의 효율적인 해법)

  • 임승환;이동춘
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.20 no.44
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    • pp.93-101
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    • 1997
  • This paper describes the application of Genetic Algorithms(GAs) to nonlinear constrained mixed optimization problems. Genetic Algorithms are combinatorial in nature, and therefore are computationally suitable for treating discrete and integer design variables. But, several problems that conventional GAs are ill defined are application of penalty function that can be adapted to transform a constrained optimization problem into an unconstrained one and premature convergence of solution. Thus, we developed an improved GAs to solve this problems, and two examples are given to demonstrate the effectiveness of the methodology developed in this paper.

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A TRUST REGION METHOD FOR SOLVING THE DECENTRALIZED STATIC OUTPUT FEEDBACK DESIGN PROBLEM

  • MOSTAFA EL-SAYED M.E.
    • Journal of applied mathematics & informatics
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    • v.18 no.1_2
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    • pp.1-23
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    • 2005
  • The decentralized static output feedback design problem is considered. A constrained trust region method is developed that solves this optimal control problem when a complete set of state variables is not available. The considered problem is interpreted as a non-linear (non-convex) constrained matrix optimization problem. Then, a decentralized constrained trust region method is developed for this problem class exploiting the diagonal structure of the problem and using inexact computations. Finally, numerical results are given for the proposed method.

A FILLED FUNCTION METHOD FOR BOX CONSTRAINED NONLINEAR INTEGER PROGRAMMING

  • Lin, Youjiang;Yang, Yongjian
    • Journal of the Korean Mathematical Society
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    • v.48 no.5
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    • pp.985-999
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    • 2011
  • A new filled function method is presented in this paper to solve box-constrained nonlinear integer programming problems. It is shown that for a given non-global local minimizer, a better local minimizer can be obtained by local search starting from an improved initial point which is obtained by locally solving a box-constrained integer programming problem. Several illustrative numerical examples are reported to show the efficiency of the present method.

An efficient method for nonlinear optimization problems using modified genetic algorithms (수정된 유전 알고리즘을 이용한 비선형최적화 문제의 효율적인 해법)

  • 윤영수;이상용
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 1996.04a
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    • pp.519-524
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    • 1996
  • This paper describes the application of Genetic Algorithms(GAs) to nonlinear constrained mixed optimization problems. Genetic Algorithms are combinatorial in nature, and therefore are computationally suitable for treating discrete and integer design variables. But, several problems that conventional GAs are ill defined are applicaiton of penalty function that can be adapted to transform a constrained optimization problem into an unconstrained optimization problem into an unconstrained one and premature convergence of solution. Thus, we developed an modified GAs to solve this problems, and two examples are given to demonstrate the effectiveness of the methodology developed in this paper.

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AN OPTIMIZATION APPROACH FOR COMPUTING A SPARSE MONO-CYCLIC POSITIVE REPRESENTATION

  • KIM, KYUNGSUP
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.20 no.3
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    • pp.225-242
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    • 2016
  • The phase-type representation is strongly connected with the positive realization in positive system. We attempt to transform phase-type representation into sparse mono-cyclic positive representation with as low order as possible. Because equivalent positive representations of a given phase-type distribution are non-unique, it is important to find a simple sparse positive representation with lower order that leads to more effective use in applications. A Hypo-Feedback-Coxian Block (HFCB) representation is a good candidate for a simple sparse representation. Our objective is to find an HFCB representation with possibly lower order, including all the eigenvalues of the original generator. We introduce an efficient nonlinear optimization method for computing an HFCB representation from a given phase-type representation. We discuss numerical problems encountered when finding efficiently a stable solution of the nonlinear constrained optimization problem. Numerical simulations are performed to show the effectiveness of the proposed algorithm.

CONFIDENCE CURVES FOR A FUNCTION OF PARAMETERS IN NONLINEAR REGRESSION

  • Kahng, Myung-Wook
    • Journal of the Korean Statistical Society
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    • v.32 no.1
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    • pp.1-10
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    • 2003
  • We consider obtaining graphical summaries of uncertainty in estimates of parameters in nonlinear models. A nonlinear constrained optimization algorithm is developed for likelihood based confidence intervals for the functions of parameters in the model The results are applied to the problem of finding significance levels in nonlinear models.

A Method using Parametric Approach for Constrained Optimization and its Application to a System of Structural Optimization Problems (제약을 갖는 최적화문제에 대한 파라메트릭 접근법과 구조문제의 최적화에 대한 응용)

  • Yang, Y.J.;Kim, W.S.
    • Journal of the Korean Operations Research and Management Science Society
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    • v.15 no.1
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    • pp.73-82
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    • 1990
  • This paper describes two algorithms to Nonlinear programming problems with equality constraints and with equality and inequality constraints. The first method treats nonlinear programming problems with equality constraints. Utilizing the nonlinear programming problems with equality constraints. Utilizing the nonlinear parametric programming technique, the method solves the problem by imbedding it into a suitable one-parameter family of problems. The second method is to solve a nonlinear programming problem with equality and inequality constraints, by minimizing a square sum of nonlinear functions which is derived from the Kuhn-Tucker condition.

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Minimum Weight Desing of Midship Structure Using Optimization Technuque (최적화 기법을 이용한 선체중앙단면의 최소중량설계)

  • J.G.,Shin
    • Bulletin of the Society of Naval Architects of Korea
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    • v.17 no.4
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    • pp.46-54
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    • 1980
  • The ship structural design problem is formulated as a general nonlinear optimization problem with constraints. Characteristics of the general structural problems and various optimization techniques are discussed, with special emphasis on penalty function method for constrained problems. A simple example of the solution of a midship structure design of cargo vessel, which complies with the rules of the Korean Register of Shipping is shown using SUMT-exterior method with some search methods.

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Robust Predictive Control of Uncertain Nonlinear System With Constrained Input

  • Son, Won-Kee;Park, Jin-Young;Kwon, Oh-Kyu
    • Transactions on Control, Automation and Systems Engineering
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    • v.4 no.4
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    • pp.289-295
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    • 2002
  • In this paper, a linear matrix inequality(LMI)-based robust control method, which combines model predictive control(MPC) with the feedback linearization(FL), is presented for constrained nonlinear systems with parameter uncertainty. The design procedures consist of the following 3 steps: Polytopic description of nonlinear system with a parameter uncertainty via FL, Mapping of actual input constraint by FL into constraint on new input of linearized system, Optimization of the constrained MPC problem based on LMI. To verify the performance and usefulness of the control method proposed in this paper, some simulations with application to a flexible single link manipulator are performed.