• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.1
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• pp.158-166
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• 2003

The paper describes the development and application of advanced evolutionary computing techniques referred to as micro genetic algorithms ($\mu$GA) in the context of engineering design optimization. The basic concept behind $\mu$GA draws from the use of small size of population irrespective of the bit string length in the representation of design variable. Such strategies also demonstrate the faster convergence capability and more savings in computational resource requirements than simple genetic algorithms (SGA). The paper first explores ten-bar truss design problems to see the optimization performance between $\mu$GA and SGA. Subsequently, $\mu$GA is applied to a realistic engineering design problem in the injection molding process optimization.

• Structural Engineering and Mechanics
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• v.64 no.5
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• pp.579-589
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• 2017

This paper deals with the optimum cost design of partially prestressed concrete I crosssectioned beams by using Genetic Algorithms. For this purpose, the optimum cost design of two selected example problems that have different characteristics in behavior are performed via Genetic Algorithms by determining their objective functions, design variables and constraints. The results obtained from the technical literature are compared with the ones obtained from this study. The interpretation of the results show that the design of partially prestressed concrete I crossectioned beams from cost point of view by using Genetic Algorithms is 35~50 % more economical than the traditional ones (technical literature) without conceding safety.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.504-507
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• 2011

3D graphic softwares have brought design spaces beyond the limitations of Euclidean space. Moreover, as computational geometry has been considered together with algorithms, generative algorithms are being evolved. Recently 3D graphic softwares with the embedded generative algorithms allow designers to design free form curves and surfaces in a systematic way. While architectural design has been greatly affected by the advancement of 3D graphic technology, such attention has not given in the realm of structural design. Grasshopper is a platform in Rhino to deal with these Generative Algorithms and Associative modelling techniques. This study has tried to develop a module for preliminary structural configuration using Rhino with Grasshopper. To verify the proposed concept in this study, a module for designing a basic type of suspension structure is introduced.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.16 no.2
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• pp.111-120
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• 2004

The optimal design method of indoor thermal environment using CFD coupled simulation and genetic algorithms (GA) is developed in this study. CFD could analyze the thermal environment considering the distribution of temperature, velocity, etc. in a room. Therefore, It would be appropriate to use CFD for the optimal design method considering their distribution. In this paper, the optimal design means the most appropriate boundary conditions of the room among the conditions where the design target of indoor therm environment is achieved. Two step optimal indoor thermal environment design method is proposed. It includes the GA for searching the optimal indoor thermal environment design. To examine the performance of this method, the optimal design of hybrid ventilation system, which uses the natural cross ventilation and the radiation-cooling panel is conducted. The optimal design which satisfies the design target (thermal comfort, minimum cooling load, minimum vertical temperature difference) is found using two step optimal design method.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.270-272
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• 2006

In this paper, we discuss optimal design of Fuzzy Polynomial Neural Networks by means of Genetic Algorithms(GAs) using symbolic coding for non-linear data. One of the major subject of genetic algorithms is representation of chromosomes. The proposed model optimized by the means genetic algorithms which used symbolic code to represent chromosomes. The proposed gFPNN used a triangle and a Gaussian-like membership function in premise part of rules and design the consequent structure by constant and regression polynomial (linear, quadratic and modified quadratic) function between input and output variables. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.207-210
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• 1999

In this paper, aperture coupled microstrip antenna which has a larger bandwidth was designed using genetic algorithms. The genetic algorithms encodes each parameters which are the width, length of patch and the width, length of slot, into binary sequences, called a gen. Genetic algorithms searches a optimal gen to design a larger bandwidth. Simulation results are compared with Pozar's results.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.535-540
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• 2006

Recently, the resizing algorithms based on the displacement participation factors have been developed for sizing members to satisfy stiffness criteria. It is proved that this resizing algorithms made for utilizing worker's stiffness design are practical and rational when applied to aseismatic design in the range of elastic until now. However, by the preceding research we confirmed that the inelastic performance of steel moment-resisting frame designed by resizing algorithms is not better than that of the frame before resizing. We present therefore a plan for improving inelastic performance of steel moment-resizing frame to which resizing algorithms applied in this paper.

• Steel and Composite Structures
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• v.24 no.1
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• pp.93-112
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• 2017

Metaheuristic algorithms in general make use of uniform random numbers in their search for optimum designs. Levy Flight (LF) is a random walk consisting of a series of consecutive random steps. The use of LF instead of uniform random numbers improves the performance of metaheuristic algorithms. In this study, three discrete optimum design algorithms are developed for steel skeletal structures each of which is based on one of the recent metaheuristic algorithms. These are biogeography-based optimization (BBO), brain storm optimization (BSO), and artificial bee colony optimization (ABC) algorithms. The optimum design problem of steel skeletal structures is formulated considering LRFD-AISC code provisions and W-sections for frames members and pipe sections for truss members are selected from available section lists. The minimum weight of steel structures is taken as the objective function. The number of steel skeletal structures is designed by using the algorithms developed and effect of LF is investigated. It is noticed that use of LF results in up to 14% lighter optimum structures.

• Journal of Korean Association for Spatial Structures
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• v.2 no.3 s.5
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• pp.61-70
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• 2002

The objective of this study is the development of a size and shape discrete optimum design algorithms, which is based on the genetic algorithms and the fuzzy theory. This algorithms can perform both size and shape optimum designs of plane and space trusses. The developed fuzzy shape-GAs (FS-GAs) was implemented in a computer program. For the optimum design, the objective function is the weight of structures and the constraints are limits on loads and serviceability. This study solves the problem by introducing the FS-GAs operators into the genetic.

• Electronics and Telecommunications Trends
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• v.4 no.1
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• pp.93-117
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• 1989

Computational Geometry is concerned with the design and analysis of computational algorithms which solve geometry problems. Geometry problems have a large number of applications areas such as pattern recognition, image processing, computer graphics, VLSI design and statistics since they involve inherently geometric problems for which efficient algorithms have to be developed. Several parallel algorithms, based on various parallel computation models, have been proposed for solving geometric problems. We review the current status of the parallel algorithms in computational geometry.