• Title/Summary/Keyword: Function optimization

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Polynomial-Filled Function Algorithm for Unconstrained Global Optimization Problems

  • Salmah;Ridwan Pandiya
    • Kyungpook Mathematical Journal
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    • v.64 no.1
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    • pp.95-111
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    • 2024
  • The filled function method is useful in solving unconstrained global optimization problems. However, depending on the type of function, and parameters used, there are limitations that cause difficultiies in implemenations. Exponential and logarithmic functions lead to the overflow effect, requiring iterative adjustment of the parameters. This paper proposes a polynomial-filled function that has a general form, is non-exponential, nonlogarithmic, non-parameteric, and continuously differentiable. With this newly proposed filled function, the aforementioned shortcomings of the filled function method can be overcome. To confirm the superiority of the proposed filled function algorithm, we apply it to a set of unconstrained global optimization problems. The data derived by numerical implementation shows that the proposed filled function can be used as an alternative algorithm when solving unconstrained global optimization problems.

SMOOTHING APPROXIMATION TO l1 EXACT PENALTY FUNCTION FOR CONSTRAINED OPTIMIZATION PROBLEMS

  • BINH, NGUYEN THANH
    • Journal of applied mathematics & informatics
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    • v.33 no.3_4
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    • pp.387-399
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    • 2015
  • In this paper, a new smoothing approximation to the l1 exact penalty function for constrained optimization problems (COP) is presented. It is shown that an optimal solution to the smoothing penalty optimization problem is an approximate optimal solution to the original optimization problem. Based on the smoothing penalty function, an algorithm is presented to solve COP, with its convergence under some conditions proved. Numerical examples illustrate that this algorithm is efficient in solving COP.

OPTIMALITY CONDITIONS AND DUALITY IN NONDIFFERENTIABLE ROBUST OPTIMIZATION PROBLEMS

  • Kim, Moon Hee
    • East Asian mathematical journal
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    • v.31 no.3
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    • pp.371-377
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    • 2015
  • We consider a nondifferentiable robust optimization problem, which has a maximum function of continuously differentiable functions and support functions as its objective function, continuously differentiable functions as its constraint functions. We prove optimality conditions for the nondifferentiable robust optimization problem. We formulate a Wolfe type dual problem for the nondifferentiable robust optimization problem and prove duality theorems.

A Practical Hull Form Optimization Method Using the Parametric Modification Function (파라메트릭 변환함수를 이용한 선형최적화의 실용화에 관한 연구)

  • Kim, Hee-Jung;Choi, Hee-Jong;Chun, Ho-Hwan
    • Journal of the Society of Naval Architects of Korea
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    • v.44 no.5
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    • pp.542-550
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    • 2007
  • A geometry modification is one of main keys in achieving a successful optimization. The optimized hull form generated from the geometry modification should be a realistic, faired form from the ship manufacturing point of view. This paper presents a practical hull optimization procedure using a parametric modification function. In the parametric modification function method, the initial ship geometry was easily deformed according to the variations of design parameters. For example, bulbous bow can be modified with several parameters such as bulb area, bulb length, bulb height etc. Design parameters are considered as design variables to modify hull form, which can reduce the number of design variables in optimization process and hence reduce its time cost. To verify the use of the parametric modification function, optimization for KCS was performed at its design speed (FN=0.26) and the wave making resistance is calculated using a well proven potential code with fully nonlinear free surface conditions. The design variables used are key design parameters such as Cp curve, section shape and bulb shape. This study shows that the hull form optimized by the parametric modification function brings 7.6% reduction in wave making resistance. In addition, for verification and comparison purpose, a direct geometry variation method using a bell-shape modification function is used. It is shown that the optimal hull form generated by the bell-shaped modification function is very similar to that produced by the parametric modification function. However, the total running time of the parametric optimization is six times shorter than that of the bell shape modification method, showing the effectiveness and practicalness from a designer point of view in ship yards.

Comparison of Cost Function of IMRT Optimization with RTP Research Tool Box (RTB)

  • Ko, Young-Eun;Yi, Byong-Yong;Lee, Sang-Wook;Ahn, Seung-Do;Kim, Jong-Hoon;Park, Eun-Kyung
    • Proceedings of the Korean Society of Medical Physics Conference
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    • 2002.09a
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    • pp.65-67
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    • 2002
  • A PC based software, the RTP Research Tool Box (RTB), was developed for IMRT optimization research. The software was consisted of an image module, a beam registration module, a dose calculation module, a dose optimization module and a dose display module. The modules and the Graphical User Interface (GUI) were designed to easily amendable by negotiating the speed of performing tasks. Each module can be easily replaced to new functions for research purpose. IDL 5.5 (RSI, USA) language was used for this software. Five major modules enable one to perform the research on the dose calculation, on the dose optimization and on the objective function. The comparison of three cost functions, such as the uncomplicated tumor control probability (UTCP), the physical objective function and the pseudo-biological objective function, which was designed in this study, were performed with the RTB. The optimizations were compared to the simulated annealing and the gradient search optimization technique for all of the optimization objective functions. No significant differences were found among the objective functions with the dose gradient search technique. But the DVH analysis showed that the pseudo-biological objective function is superior to the physical objective function when with the simulated annealing for the optimization.

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A Data Fitting Technique for Rational Function Models Using the LM Optimization Algorithm (LM 최적화 알고리즘을 이용한 유리함수 모델의 데이터 피팅)

  • Park, Jae-Han;Bae, Ji-Hun;Baeg, Moon-Hong
    • Journal of Institute of Control, Robotics and Systems
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    • v.17 no.8
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    • pp.768-776
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    • 2011
  • This paper considers a data fitting problem for rational function models using the LM (Levenberg-Marquardt) optimization method. Rational function models have various merits on representing a wide range of shapes and modeling complicated structures by polynomials of low degrees in both the numerator and denominator. However, rational functions are nonlinear in the parameter vector, thereby requiring nonlinear optimization methods to solve the fitting problem. In this paper, we propose a data fitting method for rational function models based on the LM algorithm which is renowned as an effective nonlinear optimization technique. Simulations show that the fitting results are robust against the measurement noises and uncertainties. The effectiveness of the proposed method is further demonstrated by the real application to a 3D depth camera calibration problem.

An Optimization Technique For Crane Acceleration Using A Genetic Algorithm (유전자알고리즘을 이용한 크레인가속도 최적화)

  • 박창권;김재량;정원지;홍대선;권장렬;박범석
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2003.06a
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    • pp.1701-1704
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    • 2003
  • This paper presents a new optimization technique of acceleration curve for a wafer transfer crane movement in which high speed and low vibration are desirable. This technique is based on a genetic algorithm with a penalty function for acceleration optimization under the assumption that an initial profile of acceleration curves constitutes the first generation of the genetic algorithm. Especially the penalty function consists of the violation of constraints and the number of violated constraints. The proposed penalty function makes the convergence rate of optimization process using the genetic algorithm more faster than the case of genetic algorithm without a penalty function. The optimized acceleration of the crane through the genetic algorithm and commercial dynamic analysis software has shown to have accurate movement and low vibration.

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Critical buckling load optimization of the axially graded layered uniform columns

  • Alkan, Veysel
    • Structural Engineering and Mechanics
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    • v.54 no.4
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    • pp.725-740
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    • 2015
  • This study presents critical buckling load optimization of the axially graded layered uniform columns. In the first place, characteristic equations for the critical buckling loads for all boundary conditions are obtained using the transfer matrix method. Then, for each case, square of this equation is taken as a fitness function together with constraints. Due to explicitly unavailable objective function for the critical buckling loads as a function of segment length and volume fraction of the materials, especially for the column structures with higher segment numbers, initially, prescribed value is assumed for it and then the design variables satisfying constraints are searched using Differential Evolution (DE) optimization method coupled with eigen-value routine. For constraint handling, Exterior Penalty Function formulation is adapted to the optimization cycle. Different boundary conditions are considered. The results reveal that maximum increments in the critical buckling loads are attained about 20% for cantilevered and pinned-pinned end conditions and 18% for clamped-clamped case. Finally, the strongest column structure configurations will be determined. The scientific and statistical results confirmed efficiency, reliability and robustness of the Differential Evolution optimization method and it can be used in the similar problems which especially include transcendental functions.

Extraction of Shape Information of Cost Function Using Dynamic Encoding Algorithm for Searches(DEAS) (최적화기법인 DEAS를 이용한 비용함수의 형상정보 추출)

  • Kim, Jong-Wook;Park, Young-Su;Kim, Tae-Gyu;Kim, Sang-Woo
    • Journal of Institute of Control, Robotics and Systems
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    • v.13 no.8
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    • pp.790-797
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    • 2007
  • This paper proposes a new measure of cost function ruggedness in local optimization with DEAS. DEAS is a computational optimization method developed since 2002 and has been applied to various engineering fields with success. Since DEAS is a recent optimization method which is rarely introduced in Korean, this paper first provides a brief overview and description of DEAS. In minimizing cost function with this non-gradient method, information on function shape measured automatically will enhance search capability. Considering the search strategies of DEAS are well designed with binary matrix structures, analysis of search behaviors will produce beneficial shape information. This paper deals with a simple quadratic function contained with various magnitudes of noise, and DEAS finds local minimum yielding ruggedness measure of given cost function. The proposed shape information will be directly used in improving DEAS performance in future work.

A NOVEL FILLED FUNCTION METHOD FOR GLOBAL OPTIMIZATION

  • Lin, Youjiang;Yang, Yongjian;Zhang, Liansheng
    • Journal of the Korean Mathematical Society
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    • v.47 no.6
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    • pp.1253-1267
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    • 2010
  • This paper considers the unconstrained global optimization with the revised filled function methods. The minimization sequence could leave from a local minimizer to a better minimizer of the objective function through minimizing an auxiliary function constructed at the local minimizer. Some promising numerical results are also included.