• Journal of applied mathematics & informatics
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• 제27권1_2호
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• pp.233-243
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• 2009

This paper concerns an algorithm that combines line search and trust region step for nonlinear optimization problems. Unlike traditional trust region methods, we incorporate the Armijo line search technique into trust region method to solve the subproblem. In addition, the subproblem is solved accurately, but instead solved by inaccurate method. If a trial step is not accepted, our algorithm performs the Armijo line search from the failed point to find a suitable steplength. At each iteration, the subproblem is solved only one time. In contrast to interior methods, the optimal solution is derived by iterating from outside of the feasible region. In numerical experiment, we apply the algorithm to nonlinear portfolio optimization problems, primary numerical results are presented.

• 대한기계학회논문집
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• 제18권11호
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• pp.2825-2836
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• 1994

The engineering optimization has been developed for the automatic design of engineering systems. Since the conventional optimum is determined without considering noise factors, applications to practical problems can be limited. Current design practice tends to account for these noises by the specification of closer tolerances or the use of safety factors. However, these approaches may be very expensive. Thus the consideration on the noises of design variables is needed for optimal design. A method is presented to find robust solutions for unconstrained optimization problems. The method is applied to discrete and continuous variables. The orthogonal array is utilized based on the Taguchi concept. Through mathematical proofs and numerical examples, it is verified that solutions from the suggested method are more insensitive than the conventional optimum within the range of variations for design variables.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 하계종합학술대회 논문집
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• pp.1013-1016
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• 1999

Simple Genetic Algorithm(SGA) proposed by J. H. Holland is a population-based optimization method based on the principle of the Darwinian natural selection. The theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. Although GA does well in many applications as an optimization method, still it does not guarantee the convergence to a global optimum in GA-hard problems and deceptive problems. Therefore as an alternative scheme, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve. In this paper we propose an extended schema theorem associated with a schema co-evolutionary algorithm(SCEA), which explains why the co-evolutionary algorithm works better than SGA. The experimental results show that the SCEA works well in optimization problems including deceptive functions.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2000년도 춘계학술대회논문집A
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• pp.725-730
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• 2000

Genetic algorithms based on the theory of natural selection, have been applied to many different fields, and have proven to be relatively robust means to search for global optimum and handle discontinuous or even discrete data. Genetic algorithms are widely used for unconstrained optimization problems. However, their application to constrained optimization problems remains unsettled. The most prevalent technique for coping with infeasible solutions is to penalize a population member for constraint violation. But, the weighting of a penalty for a particular problem constraint is usually determined in the heuristic way. Therefore this paper proposes, the effective technique for handling constraints, the ranking penalty method and hybrid genetic algorithms. And this paper proposes dynamic mutation tate to maintain the diversity in population. The effectiveness of the proposed algorithm is tested on several test problems and results are discussed.

• 경영과학
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• 제24권2호
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• pp.73-80
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• 2007

Robust Design(RD) is a cost-effective methodology to determine the optimal settings of control factors that make a product performance insensitive to the influence of noise factors. To better facilitate the robust design optimization, a dual response surface approach, which models both the process mean and standard deviation as separate response surfaces, has been successfully accepted by researchers and practitioners. However, the construction of response surface approximations has been limited to problems with only a few variables, mainly due to an excessive number of experimental runs necessary to fit sufficiently accurate models. In this regard, an innovative response surface approach has been proposed to investigate robust design optimization problems with larger number of variables. Response surfaces for process mean and standard deviation are partitioned and estimated based on the multi-level approximation method, which may reduce the number of experimental runs necessary for fitting response surface models to a great extent. The applicability and usefulness of proposed approach have been demonstrated through an illustrative example.

• Structural Engineering and Mechanics
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• 제8권1호
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• pp.73-84
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• 1999

This paper is concerned with shape optimization problems by the boundary element method (BEM) emphasizing the use of a reduced basis reanalysis technique proposed recently by the author. Problems of this class are conventionally carried out iteratively through an optimizer; a sequential quadratic programming-based optimizer is used in this study. The iterative process produces a succession of intermediate designs. Repeated analyses for the systems associated with these intermediate designs using an exact approach such as the LU decomposition method are time consuming if the order of the systems is large. The newly developed reanalysis technique devised for boundary element systems is utilized to enhance the computational efficiency in the repeated system solvings. Presented numerical examples on optimal shape design problems in electric potential distribution and elasticity show that the new reanalysis technique is capable of speeding up the design process without sacrificing the accuracy of the optimal solutions.

• 한국시뮬레이션학회논문지
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• 제6권1호
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• pp.71-84
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• 1997

For many optimization problems where some of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computer simulation is one of the most effective means of studying such complex systems. Many, if not most, simulation optimization problems have multiple aspects. Historically, multiple aspects have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modified to deal with multiple aspects. In this paper we propose a MAGA (Multiple Aspects-based Genetic Algorithm) as an algorithm for finding the Pareto optimal set. We demonstrate its ability to find and maintain a diverse "Pareto optimal population" on two problems.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.850-851
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• 2007

This paper presents an efficient approach for solving the economic dispatch (ED) problems with prohibited operating zones using an improved particle swarm optimization (PSO). Although the PSO-based approaches have several advantages suitable to the heavily constrained nonconvex optimization problems, they still have the drawbacks such as local optimal trapping due to the premature convergence (i.e., exploration problem) and insufficient capability to find nearly-by extreme points (i.e., exploitation problem). This paper proposes an improved PSO framework adopting a crossover operation scheme to increase both exploration and exploitation capability of the PSO. The proposed method is applied to ED problem with prohibited operating zones. Also, the results are compared with those of the state-of-the-art methods.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.559-560
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• 2021

차등 진화는 연속 최적화 문제를 해결하기 위한 효율적인 알고리즘이다. 그러나 대규모 최적화 문제를 해결하기 위해 차등 진화를 적용하면 성능이 급격히 저하되고 런타임이 기하급수적으로 증가한다. 따라서 Spark(SparkDECC로 알려짐)를 기반으로 하는 새로운 협력 공진화 차동 진화가 제안된다. 분할 정복 전략은 SparkDECC에서 사용된다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 춘계학술대회논문집C
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• pp.369-375
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• 2001

For Optimization technology Was Developed in 1960, the Optimization Technology have grown into a full-featured, robust, highly rated and highly used. And Optimization techniques, having reached a degree of maturity over the past several years, are being used in a wide spectrum of industries, including aerospace, automotive, chemical, electrical, and manufacturing industries. With rapidly advancing computer technology, computers are becoming more powerful, and correspondingly, the size and the complexity of the problems being solved using Optimization techniques are also increasing. But Optimization techniques with analysis solver have many problems. For instance, the difficulties that a particular interface must be coded for each design problem and that the designer should be familiar with the optimization program as well as the analysis program. The purpose of this paper is Optimal Design Framework for Mechanical systems design. This Design Framework has two Optimizers, ADS (local optimizer) and RSM(Response Surface Method), and graphic user interfaces for formulation and optimum design problem and controlling the design process. Current Design Framework tested by two analysis solver, ADAMS and ANSYS. First this paper focused on the core Framework and their conception. In the second of the paper, I cover subjects such as Design Framework Operation. Next, The validity and effectiveness of Design Framework are shown by applying it to many practical design problems and obtaining satisfactory results. Finally, if you are an advanced Operator, you might want to use Response Surface Method, so that cover the result applied by RSM. here.