• Communications for Statistical Applications and Methods
• /
• v.16 no.1
• /
• pp.195-208
• /
• 2009

A conventional single design optimality criterion has been used to select an efficient experimental design. But, since an experimental design is constructed with respect to an optimality criterion pre specified by investigators, an experimental design obtained from one optimality criterion which is superior to other designs may perform poorly when the design is evaluated by another optimality criterion. In other words, none of these is entirely satisfactory and even there is no guarantee that a design which is constructed from using a certain design optimality criterion is also optimal to the other design optimality criteria. Thus, it is necessary to develop certain special types of experimental designs that satisfy multiple design optimality criteria simultaneously because these multi-optimal designs (MODs) reflect the needs of the experimenters more adequately. In this article, we present a heuristic approach to construct second-order response surface designs which are more flexible and potentially very useful than the designs generated from a single design optimality criterion in many real experimental situations when several competing design optimality criteria are of interest. In this paper, over cuboidal design region for $3\;{\leq}\;k\;{\leq}\;5$ variables, we construct multi-optimal designs (MODs) that might moderately satisfy two famous alphabetic design optimality criteria, G- and IV-optimality criteria using a GA which considers a certain amount of randomness. The minimum, average and maximum scaled prediction variances for the generated response surface designs are provided. Based on the average and maximum scaled prediction variances for k = 3, 4 and 5 design variables, the MODs from a genetic algorithm (GA) have better statistical property than does the theoretically optimal designs and the MODs are more flexible and useful than single-criterion optimal designs.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.27 no.7
• /
• pp.1210-1216
• /
• 2003

Topology optimization is applied to determine the layout of a structural component with a specified frequency by minimizing the difference between the specified structural frequency and a given frequency. The homogenization design method is employed and the topology design problem is solved by the optimality criteria method. The value of a weighting factor in the optimality criteria plays an important role in this topology design problem. The modified optimality criteria method approximated by using the binomial expansion is suggested to determine the suitable value of the weighting factor, which makes convergence stable. If a given frequency is set as an excited frequency, it is possible to avoid resonance by moving away the specified structural frequency from the given frequency. The results of several test problems are compared with previous works and show the validity of the proposed algorithm.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.7 no.1
• /
• pp.23-31
• /
• 1987

This work presents an optimality criteria method which gives accurate solution to the structural optimization problem of plane frames subject to displacement and stress constraints. The method is made efficient, as well as rigorous, by including only the lateral displacement of the top floor in the set of behavioral constraints. The bending stresses of members are treated as side constraints based on the concept of fully-stressed-design, but the optimality of the final design is tested by treating them as behavioral constraints and examining if the design satisfies this new optimality criteria. Worked examples show the superiority of the rigorous opimality criteria in spite of its being simple and efficient.

• Journal of Korean Society for Quality Management
• /
• v.32 no.3
• /
• pp.234-243
• /
• 2004

In this paper, we study the optimality of 2-level resolution V minimal fractional factorial designs which can be constructed by using a partially balanced array. Moreover the relative efficiencies of such designs are compared in the sense of three optimality criteria such as determinant(D)-optimality, trace(A)-optimality, and eigenvalue(E) -optimality criterion.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.1
• /
• pp.44-51
• /
• 2002

In order to generate hull form, fairness criteria applies to object function, B-spline curve vertices are considered as design variables, optimization is proceeded with satisfying geometric constraint conditions. GA(Genetic Algorithm) and optimality criteria apply to optimization process in this study.

• Journal of the Society of Naval Architects of Korea
• /
• v.39 no.4
• /
• pp.60-65
• /
• 2002

In order to generate hull form, we introduced optimization process. Fairness criteria is applied to object function, B-Spline control vertices are considered as design variables, optimization is proceeded with satisfying geometric constraint conditions. GA(Genetic Algorithm) and optimality criteria are applied to optimization process in this study.

• Proceedings of the Korean Reliability Society Conference
• /
• 2005.06a
• /
• pp.109-113
• /
• 2005

Optimality for block designs have received much attention in the literature. Here we review these criteria and present results showing their A,D and E connection. Also we acquainted with the mathematical methods of designing optimal experiments. In this paper, we will to do work about optimality in experimental designs.

• Journal of Applied Reliability
• /
• v.17 no.2
• /
• pp.129-135
• /
• 2017

Purpose: This article provides step-stress accelerated degradation test (ADT) plans based on the Wiener process. Method: Step-stress levels and the stress change times are determined based on the D-optimality criteria to develop test plans. Further, a simple grid search method is provided for obtaining the optimal test plan. Results: Based on the solution procedure, ADT plans which include the stress levels and change times are developed for conducting the reliability test. Conclusion: Optimal step-stress ADT plans are provided for the case where the number of measurements is small.

• Journal of the Korean Society for Precision Engineering
• /
• v.19 no.11
• /
• pp.137-145
• /
• 2002

Electric vehicle body has to be subjected to uniform load and requires auxiliary equipment such as air pipe and electric wire pipe. Especially, the cross beam supports the weight of passenger and electrical equipments. a lightweight vehicle body is salutary to save operating costs and fuel consumption. Therefore this study is to perform the size and the shape optimization of crossbeam for electric vehicle using the method of topology optimization to introduce the concept of homogenization based on optimality criteria method which is efficient for the problem having the number of design variables and a few boundary condition. this provides the method to determine the optimum position and shape of circular hole in the cross beam and then can achieve the optimal design to reduce weight.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.7 no.8
• /
• pp.224-232
• /
• 1999

Topology optimization has evolved into a very efficient concept design tool and has been incorporated into design engineering processes in many industrial sectors. In recent years, topology optimization has become the focus of structural design community and has been researched and applied widely both in academia and industry. There are mainly tow approaches for topology optimization of continuum structures ; homogenization and density methods. The homogenization method is to compute is to compute an optimal distribution of microstructures in a given design domain. The sizes of the micro-calvities are treated as design variables for the topology optimization problem. the density method is to compute an optimal distribution of an isotropic material, where the material densities are treated as design variables. In this paper, the density method is used to formulate the topology optimization problem. This optimization problem is solved by using an optimality criteria method. Several example problems are solved to show the usefulness of the present approach.