• 한국광학회지
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• 제20권6호
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• pp.319-327
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• 2009

본 논문에서는 다음과 같은 관점의 광각 2군 줌 렌즈 설계를 소개한다. 먼저전역최적화의 개념을 기초설계단계에서 도입하고, 이를 통해 현대의 수많은 데이터들을 체계적으로 계통화하고 단순화할 수 있는 설계방안을 제안한다. 구체적인 방안으로 전역설 계를 위해 전역구조함수라는 새로운 개념의 함수를 도입하였고 단순화시켰으며, 나아가 약간의 대수적인 혹은 수치적인 계산을 통해 전역 해 영역을 구하였다. 전역 해 영역은 전역최적화에 대응되는 개념이고 상용화된 설계프로그램들 보다 더 체계적이고 통찰적인 설계방향을 제시한다.

• ETRI Journal
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• 제46권3호
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• pp.367-378
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• 2024

We propose a number-theory-based quantized mathematical optimization scheme for various NP-hard and similar problems. Conventional global optimization schemes, such as simulated and quantum annealing, assume stochastic properties that require multiple attempts. Although our quantization-based optimization proposal also depends on stochastic features (i.e., the white-noise hypothesis), it provides a more reliable optimization performance. Our numerical analysis equates quantization-based optimization to quantum annealing, and its quantization property effectively provides global optimization by decreasing the measure of the level sets associated with the objective function. Consequently, the proposed combinatorial optimization method allows the removal of the acceptance probability used in conventional heuristic algorithms to provide a more effective optimization. Numerical experiments show that the proposed algorithm determines the global optimum in less operational time than conventional schemes.

• 한국CDE학회논문집
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• 제15권1호
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• pp.24-32
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• 2010

This paper introduces a B-spline based branch & bound algorithm for global optimization. The branch & bound is a well-known algorithm paradigm for global optimization, of which key components are the subdivision scheme and the bound calculation scheme. For this, we consider the B-spline hypervolume to approximate an objective function defined in a design space. This model enables us to subdivide the design space, and to compute the upper & lower bound of each subspace where the bound calculation is based on the LHS sampling points. We also describe a search tree to represent the searching process for optimal solution, and explain iteration steps and some conditions necessary to carry out the algorithm. Finally, the performance of the proposed algorithm is examined on some test problems which would cover most difficulties faced in global optimization area. It shows that the proposed algorithm is complete algorithm not using heuristics, provides an approximate global solution within prescribed tolerances, and has the good possibility for large scale NP-hard optimization.

• 대한조선학회논문집
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• 제45권6호
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• pp.590-600
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• 2008

This paper concerns the development of a designer friendly hull form parameterization and its coupling with advanced global optimization algorithms. As optimization algorithms, we choose the Partial Swarm Optimization(PSO) recently introduced to solve global optimization problems. Most general-purpose optimization softwares used in industrial applications use gradient-based algorithms, mainly due to their convergence properties and computational efficiency when a relatively few number of variables are considered. However, local optimizers have difficulties with local minima and non-connected feasible regions. Because of the increase of computer power and of the development of efficient Global Optimization (GO) methods, in recent years nongradient-based algorithms have attracted much attention. Furthermore, GO methods provide several advantages over local approaches. In the paper, the derivative-based SQP and the GO approach PSO are compared with their relative performances in solving some typical ship design optimization problem focusing on their effectiveness and efficiency.

• 대한기계학회논문집A
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• 제29권9호
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• pp.1243-1252
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• 2005

A current trend of design methodologies is to make engineers objectify or automate the decision-making process. Numerical optimization is an example of such technologies. However, in numerical optimization, the uncertainties are uncontrollable to efficiently objectify or automate the process. To better manage these uncertainties, the Taguchi method, reliability-based optimization and robust optimization are being used. To obtain the target performance with the maximum robustness is the main functional requirement of a mechanical system. In this research, a design procedure for global robust optimization is developed based on the kriging and global optimization approaches. The DACE modeling, known as the one of Kriging interpolation, is introduced to obtain the surrogate approximation model of the function. Robustness is determined by the DACE model to reduce real function calculations. The simulated annealing algorithm of global optimization methods is adopted to determine the global robust design of a surrogated model. As the postprocess, the first order second-moment approximation method is applied to refine the robust optimum. The mathematical problems and the MEMS design problem are investigated to show the validity of the proposed method.

• Journal of international Conference on Electrical Machines and Systems
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• 제1권1호
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• pp.32-39
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• 2012

With the aid of genetic algorithm (GA), global optimization with multiple geometry parameters is feasible in the design of switched flux permanent magnet (SFPM) machines. To investigate the advantages of global optimization over individual optimization, which has been used extensively for the design of SFPM machines, a comparison between the two approaches is carried out for the case of fixed copper loss and volume. In the case of individual parameter optimization, the sequence in which the individual parameters are optimized is very important. In the global optimization a better design can always be achieved although the corresponding torque density is found to be only slightly better than that of individually optimized with correct design sequence. By using the obtained global optimization results, the performance in machines having two types of stator and rotor pole combinations, i.e. 12/10 and 12/14, are compared, and it is shown that higher torque is exhibited in the 12/14 SFPM machine. Finally, this paper also demonstrates that global optimization, with the restriction of equal pole width, magnet thickness and slot opening, can maximize the torque density without significantly sacrificing other performance, such as cogging torque and overload capability.

• Kyungpook Mathematical Journal
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• 제64권1호
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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.

• Journal of Mechanical Science and Technology
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• 제20권10호
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• pp.1557-1566
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• 2006

We present a procedure for the application of global orthogonal polynomial into an atmospheric re-entry maneuvering problem. This trajectory optimization is imbedded in a family of canonically parameterized optimal control problem. The optimal control problem is transcribed to nonlinear programming via global orthogonal polynomial and is solved a sparse nonlinear optimization algorithm. We analyze the optimal trajectories with respect to the performance of re-entry maneuver.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 추계학술대회
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• pp.870-875
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• 2004

Current trend of design technologies shows engineers to objectify or automate the given decision-making process. The numerical optimization is an example of such technologies. However, in numerical optimization, the uncertainties are uncontrollable to efficiently objectify or automate the process. To better manage these uncertainties, Taguchi method, reliability-based optimization and robust optimization are being used. To obtain the target performance with the maximum robustness is the main functional requirement of a mechanical system. In this research, the robust design strategy is developed based on the DACE and the global optimization approaches. The DACE modeling, known as the one of Kriging interpolation, is introduced to obtain the surrogate approximation model of the system. The robustness is determined by the DACE model to reduce the real function calculations. The simulated annealing algorithm of global optimization methods is adopted to determine the global robust design of a surrogated model. The mathematical problems and the MEMS design problem are investigated to show the validity of the proposed method.

• 전기학회논문지
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• 제57권5호
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• pp.748-754
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• 2008

The general capacitor placement problem is a combinatorial optimization problem having an objective function composed of power losses and capacitor installation costs subject to bus voltage constraints. In this paper, a global optimization technique, which employing the chaos search algorithm, is applied to solve optimal capacitor placement problem with reducing computational effort and enhancing global optimality of the solution. Chaos method in optimization problem searches the global optimal solution on the regularity of chaotic motions and easily escapes from local or near optimal solution than stochastic optimization algorithms. The chaos optimization method is tested on 9 buses and 69 buses system to illustrate the effectiveness of the proposed method.