• Proceedings of the Computational Structural Engineering Institute Conference
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• 1995.04a
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• pp.195-202
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• 1995

In this study, the optimization program is developed to provide preliminary designs of steel-box girder bridges with minimum cost. The advantages of steel-box girder deck, when comparing with other girder types, are higher torsional rigidity and better resistance against corrosion. To achieve more rational design, systematic design procedure is required, by which the design constraints on steel-box girder are satisfied and the design variables with minimum cost are obtained. In the Proposed optmum design Process, the design variables are forced to be selected from the available discrete value set. The efficiency of the developed program has been verified by companing with previous designed sections and the resulting optimum cost with discrete variables has been compared with those of continuous variables.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.391-393
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• 2004

For performance and stability of a synchronized system, we need an efficient Clock Tree Synthesis(CTS) methodology to design clock distribution networks. In a system-on-a-chip(SOC) design environment, CTS effectively distributes clock signals from clock sources to synchronized points on layout design. In this paper, we suggest the pre-layout analysis of the clock network including gated clock, multiple clock, and test mode CTS optimization. This analysis can help to avoid design failure with potential CTS problems from logic designers and supply layout constraints so as to get an optimal clock distribution network. Our new design flow including pre-layout CTS analysis and structural violation checking also contributes to reduce design time significantly.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.28 no.10
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• pp.1558-1565
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• 2004

It is important to determine supporting locations for structural stability when a structure is loaded with non-uniform load or supporting locations as well as the number of the supporting structures are restricted by the problem of space. Moreover, the supporting location optimization of complex structure in real world is frequently faced with discontinuous design space. Therefore, the traditional optimization methods based on derivative are not suitable Whereas, Genetic Algorithm (CA) based on stochastic search technique is a very robust and general method. The KSTAR in-vessel control coil installed in vacuum vessel is loaded with non- uniform electro-magnetic load and supporting locations are restricted by the problem of space. This paper shows the supporting location optimization for structural stability of the in-vessel control coil. Optimization has been performed by means of a developed program. It consists of a Finite Element Analysis interfaced with a Genetic Algorithm. In addition, this paper presents an algorithm to find an optimum solution in discontinuous space using continuous design variables.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.15 no.3
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• pp.453-462
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• 2002

Discrete-sizing or standardized steel profiles are used in steel design and construction practice. However, most of numerical optimization methods follow additional step(round-up discrete-sizing routine) to use the standardized steel section profiles, and accordingly the optimality of the resulting design nay be doubtful. Thus, in this paper, an efficient multi-level optimization algorithm is proposed to improve the shortcoming of the conventional optimization methods using the round-up discrete-sizing routine. Also, multi-level optimization technique with a decomposition method that separates both system-level and element-level is incorporated in the algorithm to enhance the performance of the proposed algorithms. The proposed algorithm is expected to achieve considerable improvement on both the efficiency of the numerical process and the accuracy of the global optimum.

• Structural Engineering and Mechanics
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• v.53 no.5
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• pp.847-865
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• 2015

This paper presents the application of a recently developed meta-heuristic algorithm, called Colliding Bodies Optimization (CBO), for size and topology optimization of steel trusses. This method is based on the one-dimensional collisions between two bodies, where each agent solution is considered as a body. The performance of the proposed algorithm is investigated through four benchmark trusses for minimum weight with static and dynamic constraints. A comparison of the numerical results of the CBO with those of other available algorithms indicates that the proposed technique is capable of locating promising solutions using lesser or identical computational effort, with no need for internal parameter tuning.

• Structural Engineering and Mechanics
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• v.58 no.6
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• pp.1109-1126
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• 2016

The present paper focuses on size optimization of double layer barrel vaults considering nonlinear behavior. In order to tackle the optimization problem an improved colliding bodies optimization (ICBO) algorithm is proposed. The important task that should be achieved before optimization of structural systems is to determine the best form having the least cost. In this study, an attempt is done to find the best form then it is optimized considering linear and non-linear behaviors. In the optimization process based on nonlinear behavior, the geometrical and material nonlinearity effects are included. A large-scale double layer barrel vault is presented as the numerical example of this study and the obtained results indicate that the proposed ICBO has better computational performance compared with other algorithms.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.8 s.239
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• pp.1051-1060
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• 2005

Nonlinear Response Optimization using Equivalent Static Loads (NROESL) method/algorithm is proposed to perform optimization of non-linear response structures. The conventional method spends most of the total design time on nonlinear analysis. The NROESL algorithm makes the equivalent static load cases for each response and repeatedly performs linear response optimization and uses them as multiple loading conditions. The equivalent static loads are defined as the loads in the linear analysis, which generates the same response field as those in non-linear analysis. The algorithm is validated for the convergence and the optimality. The proposed algorithm is applied to a simple mathematical problem to verify the convergence and the optimality.

• Advances in Computational Design
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• v.1 no.2
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• pp.175-194
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• 2016

Nature has provided inspiration for most of the man-made technologies. Scientists believe that dolphins are the second to humans in smartness and intelligence. Echolocation is the biological sonar used by dolphins for navigation and hunting in various environments. This ability of dolphins is mimicked in this paper to develop a new optimization method. Dolphin Echolocation Optimization (DEO) is an optimization method based on dolphin's approach for hunting food and exploration of environment. DEO has already been developed for discrete optimization search space and here it is extended to continuous search space. DEO has simple rules and is adjustable for predetermined computational cost. DEO provides the optimum results and leads to alternative optimality curves suitable for the problem. This algorithm has a few parameters and it is applicable to a wide range of problems like other metaheuristic algorithms. In the present work, the efficiency of this approach is demonstrated using standard benchmark problems.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.10a
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• pp.141-148
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• 2000

In system design, it is not always possible that all decision makers can cooperate fully and thus avoid conflict. They each control a specified subset of design variables and seek to minimize their own cost functions subject to their individual constraints. However, a system management team makes every effort to coordinate multiple disciplines and overcome such noncooperative environment. Although full cooperation is difficult to achieve, noncooperation also should be avoided as possible. Our approach is to predict the results of their cooperation and generate approximate Pareto set for their multiple objectives. The Pareto set can be obtained according to the degree of one's conceding coupling variables in the other's favor. We employ approximation concept for modelling this coordination and the mutiobjective genetic algorithm for exploring the coupling variable space for obtaining an approximate Pareto set. The approximation management concept is also used for improving the accuracy of the Pareto set. The exploration for the coupling variable space is more efficient because of its smaller dimension than the design variable space. Also, our approach doesn't force the disciplines to change their own way of running analysis and synthesis tools. Since the decision making process is not sequential, the required time can be reduced comparing to the existing multidisciplinary optimization techniques. This approach is applied to some mathematical examples and structural optimization problems.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1999.10a
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• pp.333-342
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• 1999

An improved optimization algorithm for multi-objective and multi-level (MO/ML) optimum design of steel frames is proposed in this paper. In order to optimize the steel frames under seismic load, two main objective functions need to be considered for minimizing the structural weight and maximizing the strain energy. For the efficiency of the proposed method, well known multi-level optimization techniques using decomposition method that separately utilizes both system-level and element-level optimizations and an artificial constraint deletion technique are incorporated in the algorithm. And also dynamic analysis is executed to evaluate the implicit function of structural strain energy at each iteration step. To save the numerical efforts, an efficient reanalysis technique through sensitivity analysis of dynamic properties is unposed in the paper. The efficiency and robustness of the improved MOML algorithm, compared with a plain MOML algorithm, is successfully demonstrated in the numerical examples.