• Journal of Korean Society of Steel Construction
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• v.17 no.4 s.77
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• pp.385-394
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• 2005

In this paper, a discrete optimum design program was developed using the refined fuzzy-genetic algorithms based on the genetic algorithms and the fuzzy theory. The optimum design in this study can perform section and shape optimization simultaneously for planar and spatial steel structures. In this paper, the objective function is the weight of steel structures and the constraints are the design limits defined by the design and buckling strengths, displacements, and thicknesses of the member sections. The design variables are the dimensions and coordinates of the steel sections. Design examples are given to show the applicability of the discrete optimum design using the improved fuzzy-genetic algorithms in this study.

• Journal of Ocean Engineering and Technology
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• v.24 no.6
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• pp.109-113
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• 2010

We present the minimum weight optimum design of cross sectional for frame structures subject to natural frequency. The optimum design in this paper employ discrete and continuous design variables and Genetic Algorithms. In this paper, Genetic Algorithms is used in optimization process, and be used the method of Elitism and penalty parameters in order to improved fitness in the reproduction process. For 1-Bay 2-Story frame structure, in examples, continuous and discrete design variables are used, and W-section (No.1~No.64), from AISC, discrete data are used in discrete optimization. In this case, Exhaustive search are used for finding global optimum. Continuous variables are used for 1-Bay 7-Story frame structure. Two typical frame structure optimization examples are employed to demonstrate the availability of Genetic Algorithms for solving minimum weight optimum of frame structures with fundamental and multi frequency.

• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.6
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• pp.602-614
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• 2016

It is necessary to develop an efficient optimization technique to perform optimum designs which have given design spaces, discrete design values and several design goals. As optimization techniques, direct search method and stochastic search method are widely used in designing of ship structures. The merit of the direct search method is to search the optimum points rapidly by considering the search direction, step size and convergence limit. And the merit of the stochastic search method is to obtain the global optimum points well by spreading points randomly entire the design spaces. In this paper, Pareto Strategy (PS) multi-objective function method is developed by considering the search direction based on Pareto optimal points, the step size, the convergence limit and the random number generation. The success points between just before and current Pareto optimal points are considered. PS method can also apply to the single objective function problems, and can consider the discrete design variables such as plate thickness, longitudinal space, web height and web space. The optimum design results are compared with existing Random Search (RS) multi-objective function method and Evolutionary Strategy (ES) multi-objective function method by performing the optimum designs of double bottom structure and double hull tanker which have discrete design values. Its superiority and effectiveness are shown by comparing the optimum results with those of RS method and ES method.

• Journal of Korean Society of Steel Construction
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• v.19 no.2
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• pp.171-179
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• 2007

The greenhouse discrete optimum design program was developed using discrete optimum algorithm based on the genetic algorithm. The basic search method for the optimum design is the genetic algorithm, which is known to be very efficient for discrete optimization. In this paper, the objective function was the weight of the greenhouse structures and the constraints were the limits state design method. The design variables were galvanized steel pipes for plastic housing KSD 3760. Objective criteria were presented for the design of economic greenhouse structure and evaluation of its stability. The standardizations of greenhouse structure were used, as well as the normalization of greenhouse-related materials. Design examples were given to show the applicability of the optimum design using the discrete optimum algorithm based on the genetic algorithm of this study.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.1
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• pp.122-130
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• 2001

In order to improve the efficiency of the design process and the quality of the resulting design, this study proposes a design method for determining design variables of an automotive wheel-bearing unit of double-row angular-contact ball bearing type by using a genetic algorithm. The desired performance of the wheel-bearing unit is to maximize system life while satisfying geometrical and operational constraints without enlarging mounting spae. The use of gradient-based optimization methods for the design of the unit is restricted because this design problem is characterized by the presence of discrete design variables such as the number of balls and standard ball diameter. Therefore, the design problem of rolling element bearings is a constrained discrete optimization problem. A genetic algorithm using real coding and dynamic mutation rate is used to efficiently find the optimum discrete design values. To effectively deal with the design constraints, a ranking method is suggested for constructing a fitness function in the genetic algorithm. A computer program is developed and applied to the design of a real wheel-bearing unit model to evaluate the proposed design method. Optimum design results demonstrate the effectiveness of the design method suggested in this study by showing that the system life of an optimally designed wheel-bearing unit is enhanced in comparison with that of the current design without any constraint violations.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.5
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• pp.19-26
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• 2010

This study was conducted to find out the reasonable optimum design method of agricultural reinforced concrete structures. Selected design variables are the dimension of concrete section, reinforced steel area, and objective function is formulated by cost function. To test the reliability, efficiency, possibility of application and reasonability of optimum design method, both continuous optimization method and mixed-discrete optimization method were applied to the design of reinforced concrete superstructure of aqueduct and application results were discussed. It is proved that mixed-discrete optimization method is more reliable, efficient and reasonable than continuous optimization method for the optimum design of reinforced concrete agricultural structures.

• Journal of Ocean Engineering and Technology
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• v.21 no.2 s.75
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• pp.75-80
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• 2007

The design variables for structural systems, in most practical designs, are chosen from a list of discrete values, which are commercially available sizing. This paper presents the application of Genetic Algorithms for determining the optimum design for two-dimensional structures with discrete and pseudocontinuous design variables. Genetic Algorithms are heuristic search algorithms and are effective tools for finding global solutions for discrete optimization. In this paper, Genetic Algorithms are used as the method of Elitism and penalty parameters, in order to improve fitness in the reproduction process. Examples in this paper include: 10 bar planar truss and 1 bay 8-story frame. Truss with discrete and pseudoucontinuous design variables and steel frame with W-sections are used for the design of discrete optimization.

• Journal of the Society of Naval Architects of Korea
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• v.31 no.4
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• pp.147-156
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• 1994

Though optimization method had been used for long time for the optimal design of ship structure, design variables in the most cases were assumed to be continuous real values or it was not easy to solve the mixed integer optimum design problems using the conventional optimization methods. Thus, it was often tried to use various initial starting points to locate the best optimum paint and to use special method such as branch and bound method to handle the discrete design variables in the optimization problems. Sometimes it had succeed, but the essential problems for dealing with the local optimum and discrete design variables was left unsolved. Hence, in this paper, Genetic Algorithms adopting the biological evolution process is applied to the ship structural design problem where the integer values for the number of stiffen design variables or the discrete values for the plate thickness variables would be more preferable in order to find out their effects on the final optimum design. Through the numerical result comparisons, it was found that Genetic Algorithm could always yield the global optimum for the discrete and mixed integer structural optimization problem cases even though it takes more time than other methods.

• Journal of The Korean Society of Agricultural Engineers
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• v.52 no.6
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• pp.49-56
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• 2010

This study was conducted to find out the best optimum design method for the design of reinforced concrete agricultural aqueduct abutment and pier structures. The mixed-discrete optimization and continuous optimization method were applied to the design of reinforced concrete agricultural aqueduct abutment and pier and the results of these optimization methods were compared each other. It is proved that mixed-discrete optimization method is more reliable, efficient and reasonable than continuous optimization method for the optimum design of the reinforced concrete agricultural aqueduct abutment and pier.

• Journal of Korean Society of Steel Construction
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• v.13 no.6
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• pp.673-681
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• 2001

The objective of this study is the development of size, shape and topology discrete optimum design algorithm which is based on the genetic algorithm. The algorithm can perform both shape and topology optimum designs of trusses. The developed algorithm was implemented in a computer program. For the optimum design, the objective function is the weight of trusses and the constraints are stress and displacement. The basic search method for the optimum design is the genetic algorithm. The algorithm is known to be very efficient for the discrete optimization. The genetic algorithm consists of genetic process and evolutionary process. The genetic process selects the next design points based on the survivability of the design points selected form the genetic process. The evolutionary process evaluates the survivability of the design points. The evolutionary process evaluates the survivability of the design points selected form the genetic process. The efficiency and validity of the developed size, shape and topology discrete optimum design algorithm was verified by applying the algorithm to optimum design examples.