• Structural Engineering and Mechanics
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• v.10 no.4
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• pp.313-336
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• 2000

The focus of this paper is on the development and implementation of a methodology for automated design of discrete structural systems. The research is aimed at utilizing Genetic Algorithms (GA) as an automated design tool. Several key enhancements are made to the simple GA in order to increase the efficiency, reliability and accuracy of the methodology for code-based design of structures. The AISC-ASD design code is used to illustrate the design methodology. Small as well as large-scale problems are solved. Simultaneous sizing, shape and topology optimal designs of structural framed systems subjected to static and dynamic loads are considered. Comparisons with results from prior publications and solution to new problems show that the enhancements made to the GA do indeed make the design system more efficient and robust.

• Proceedings of the KSME Conference
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• 2001.06c
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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.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.5
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• pp.798-806
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• 2003

Structural optimization has been carried out in continuous or discrete design space. Methods for continuous design have been well developed though they are finding the local optima. On the contrary, the existing methods for discrete design are extremely expensive in computational cost or not robust. In this research, an algorithm using tabu search is developed fur the discrete structural designs. The tabu list and the neighbor function of the Tabu concepts are introduced to the algorithm. It defines the number of steps, the maximum number for random searches and the stop criteria. A tabu search is known as the heuristic approach while genetic algorithm and simulated annealing algorithm are attributed to the stochastic approach. It is shown that an algorithm using the tabu search with random moves has an advantage of discrete design. Furthermore, the suggested method finds the reliable optimum for the discrete design problems. The existing tabu search methods are reviewed. Subsequently, the suggested method is explained. The mathematical problems and structural design problems are investigated to show the validity of the proposed method. The results of the structural designs are compared with those from a genetic algorithm and an orthogonal array design.

• Computational Structural Engineering
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• v.8 no.2
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• pp.123-132
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• 1995

Genetic Algorithms(GA), which are based on the theory of natural evolution, have been evaluated highly for their robust performances. Traditional GA has mostly used binary code for representing design variable. The binary code GA has many difficulties to solve optimization problems with continuous design variables because of its large computer core memory size, inefficiency of its computing time, and its bad performance on local search. In this paper, a real code GA is proposed for dealing with the above problems. So, new crossover and mutation processes of GA are developed to use continuous design variables directly. The results of read code GA are compared with those of binary code GA for several single and multiple objective optimization problems. As a result of comparisons, it is found that the performance of the real code GA is better than that of the binary code GA, and concluded that the real code GA developed here can be used for the general optimization problem.

• Structural Engineering and Mechanics
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• v.63 no.5
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• pp.607-615
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• 2017

This study presents optimum design of plane steel bridges considering corrosion effect by using teaching-learning based optimization (TLBO) method. Optimum solutions of three different bridge problems are linearly carried out including and excluding corrosion effect. The member cross sections are selected from a pre-specified list of 128 W profiles taken from American Institute of Steel Construction (AISC). A computer program is coded in MATLAB to carry out optimum design interacting with SAP2000 using OAPI (Open Application Programming Interface). The stress constraints are incorporated as indicated in AISC Allowable Stress Design (ASD) specifications and also displacement constraints are applied in optimum design. The results obtained from analysis show that the corrosion effect on steel profile surfaces causes a crucial increase on the minimum steel weight of bridges. Moreover, the results show that the method proposed is applicable and robust to reach the destination even for complex problems.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.109-112
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• 1997

In this note, we investigate a noniterative design method of an observer-based robust H$\_$2/ controller in the presence of structured real parameter uncertainty by applying Riccati approach based on the guaranteed cost function. Motivated by the numerical difficulty of the problem, we try to develop a simple design method named as block-diagonal approach, which can be solved by the LMIs method. By assuming the block-diagonal structure of Riccati solution, the original problem can be derived into two sequentially decoupled optimization problems as LQG control problem. The proposed method seems to be numerically efficient in obtaining a feasible compensator.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.2
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• pp.129-139
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• 2016

This paper presented a robust ship scheduling model using the quadratic programming problem. Given a set of available carriers under control and a set of cargoes to be transported from origin to destination, a robust ship scheduling that can minimize the mean-variance objective function with the required level of profit can be modeled. Computational experiments concerning relevant maritime transportation problems are performed on randomly generated configurations of tanker scheduling in bulk trade. In the first stage, the optimal transportation problem to achieve maximum revenue is solved through the traditional set-packing model that includes all feasible schedules for each carrier. In the second stage, the robust ship scheduling problem is formulated as mentioned in the quadratic programming. Single index model is used to efficiently calculate the variance-covariance matrix of objective function. Significant results are reported to validate that the proposed model can be utilized in the decision problem of ship scheduling after considering robustness and the required level of profit.

• Structural Engineering and Mechanics
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• v.71 no.1
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• pp.1-12
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• 2019

This study investigates optimum designs of steel space truss towers under seismic loading by using Jaya optimization algorithm. Turkish Earthquake Code (2007) specifications are applied on optimum designs of steel space truss towers under the seismic loading for different local site classes depending on different soil groups. The proposed novel algorithm does not have any algorithm-specific control parameters and depends only a simple revision equation. Therefore, it provides a practical solution for structural optimization problems. Optimum solutions of the different steel truss examples are carried out by selecting suitable W sections taken from American Institute of Steel Construction (AISC). In order to obtain optimum solutions, a computer program is coded in MATLAB in corporated with SAP2000-OAPI (Open Application Programming Interface). The stress and displacement constraints are applied on the design problems according to AISC-ASD (Allowable Stress Design) specifications. Firstly, a benchmark truss problem is examined to see the efficiency of Jaya optimization algorithm. Then, two different multi-element truss towers previously solved with other methods without seismic loading in literature are designed by the proposed algorithm. The first space tower is a 582-member space truss with the height of 80 m and the second space tower is a 942-member space truss of about 95 m height. The minimum optimum designs obtained with this novel algorithm for the case without seismic loading are lighter than the ones previously attained in the literature studies. The results obtained in the study show that Jaya algorithm is a practical and robust optimization method for structural optimization problems. Moreover, incorporation of the seismic loading causes significant increase in the minimum design weight.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.22 no.4
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• pp.811-825
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• 1998

An approximate line search is presented for dynamic response optimization with Augmented Lagrange Multiplier(ALM) method. This study empolys the approximate a augmented Lagrangian, which can improve the efficiency of the ALM method, while maintaining the global convergence of the ALM method. Although the approximate augmented Lagragian is composed of only the linearized cost and constraint functions, the quality of this approximation should be good since an approximate penalty term is found to have almost second-order accuracy near the optimum. Typical unconstrained optimization algorithms such as quasi-Newton and conjugate gradient methods are directly used to find exact search directions and a golden section method followed by a cubic polynomial approximation is empolyed for approximate line search since the approximate augmented Lagrangian is a nonlinear function of design variable vector. The numberical performance of the proposed approach is investigated by solving three typical dynamic response optimization problems and comparing the results with those in the literature. This comparison shows that the suggested approach is robust and efficient.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.199-208
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• 2023

Taguchi method is one of the most popular approaches for design optimization such that performance characteristics become robust to uncontrollable noise variables. However, most previous Taguchi method applications have addressed a single-characteristic problem. Problems with multiple characteristics are more common in practice. The multi-criteria decision making(MCDM) problem is to select the optimal one among multiple alternatives by integrating a number of criteria that may conflict with each other. Representative MCDM methods include TOPSIS(Technique for Order of Preference by Similarity to Ideal Solution), GRA(Grey Relational Analysis), PCA(Principal Component Analysis), fuzzy logic system, and so on. Therefore, numerous approaches have been conducted to deal with the multi-characteristic design problem by combining original Taguchi method and MCDM methods. In the MCDM problem, multiple criteria generally have different measurement units, which means that there may be a large difference in the physical value of the criteria and ultimately makes it difficult to integrate the measurements for the criteria. Therefore, the normalization technique is usually utilized to convert different units of criteria into one identical unit. There are four normalization techniques commonly used in MCDM problems, including vector normalization, linear scale transformation(max-min, max, or sum). However, the normalization techniques have several shortcomings and do not adequately incorporate the practical matters. For example, if certain alternative has maximum value of data for certain criterion, this alternative is considered as the solution in original process. However, if the maximum value of data does not satisfy the required degree of fulfillment of designer or customer, the alternative may not be considered as the solution. To solve this problem, this paper employs the desirability function that has been proposed in our previous research. The desirability function uses upper limit and lower limit in normalization process. The threshold points for establishing upper or lower limits let us know what degree of fulfillment of designer or customer is. This paper proposes a new design optimization technique for multi-characteristic design problem by integrating the Taguchi method and our desirability functions. Finally, the proposed technique is able to obtain the optimal solution that is robust to multi-characteristic performances.