• Steel and Composite Structures
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• v.14 no.5
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• pp.431-451
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• 2013

The Big Bang-Big Crunch (BB-BC) optimization algorithm is developed for optimal design of non-linear steel frames with semi-rigid beam-to-column connections. The design algorithm obtains the minimum total cost which comprises total member plus connection costs by selecting suitable sections. Displacement and stress constraints together with the geometry constraints are imposed on the frame in the optimum design procedure. In addition, non-linear analyses considering the P-${\Delta}$ effects of beam-column members are performed during the optimization process. Three design examples with various types of connections are presented and the results show the efficiency of using semi-rigid connection models in comparing to rigid connections. The obtained optimum semi-rigid frames are more economical solutions and lead to more realistic predictions of response and strength of the structure.

• Journal of Korean Association for Spatial Structures
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• v.4 no.2 s.12
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• pp.89-97
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• 2004

The objective of this study is the development of a scheme and discrete optimum design algorithm, which is based on the genetic algorithm. The algorithm can perform both scheme and size optimum designs of plane trusses. The developed Scheme genetic algorithm 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. The basic search method for the optimum design is the genetic algorithm. The algorithm is known to be very efficient for the discrete optimization. However, its application to the complicated structures has been limited because of the extreme time need for a number of structural analyses. This study solves the problem by introducing the size & scheme genetic algorithm operators into the genetic algorithm. The genetic process virtually takes no time. However, the evolutionary process requires a tremendous amount of time for a number of structural analyses. Therefore, the application of the genetic algorithm to the complicated structures is extremely difficult, if not impossible. The scheme genetic algorithm operators was introduced to overcome the problem and to complement the evolutionary process. It is very efficient in the approximate analyses and scheme and size optimization of plane trusses structures and considerably reduces structural analysis time. Scheme and size discrete optimum combined into the genetic algorithm is what makes the practical discrete optimum design of plane fusses structures possible. The efficiency and validity of the developed discrete optimum design algorithm was verified by applying the algorithm to various optimum design examples: plane pratt, howe and warren truss.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2004.04a
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• pp.151-160
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• 2004

Genetic algorithm is the theory of grafting the principle of survival of the fittest in genetics on to the computer algorithm and it is used to solve the optimization problems, especially the shape and size optimization of the structure in Architectural problems. In the size optimization problem discrete variables are used, but series variables have to be used in the shape optimization problem because of the incongruenty. The purpose of this study is to obtain the optimum shape of cable domes by using the real coding genetic algorithm. Generally, the structural performance of the cable domes is influenced very sensitively by pre-stress, geometry and length of the mast because of its flexible characteristic. So, it is very important to decide the optimum shape to get maximum stiffness of cable domes. We use the model to verify the usefulness of this algorithm for shape optimization and analyze the roof system of Seoul Olympic Gymnastic Arena as analytical model of a practical structures. It is confirmed lastly that the optimum shape domes have more stiffness than initial shape ones.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.6
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• pp.1819-1826
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• 1996

This paper present the claculating method of optimum correction mass within permissible vibration linits for ratating machinery in two-plane field balancing. Basic technique of this method is based on influence coefficient method, and grphic vector composition that the resultant of two influence vectors obtained by trial mass have to be equilibrium with initial vibration vector in the each correction plane. Genetic algorithm which is a search algorithm based on the mechanism of natural selection and natural genetics is sued for vector composition, and SUMT method is used to objective function which seeks optimum correction mass for balancing a rotor.

• Journal of Korean Institute of Industrial Engineers
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• v.12 no.1
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• pp.1-11
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• 1986

This research is concerned with n jobs, m parallel identical machines scheduling problem in which all jobs have a common due date. The objective of the research is to develop an optimum scheduling algorithm for determining an optimal job sequence, the optimal value of the common due date and the minimum makespan to minimize total cost. The total cost is based on the common due date cost, the earliness cost, the tardiness cost and the flow time cost of each job in the selected sequence. The optimum scheduling algorithm is developed. A numerical example is given to illustrate the scheduling algorithm.

• Proceedings of the IEEK Conference
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• 2002.06b
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• pp.109-112
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• 2002

This paper presents relationship between the dynamic behavior of an asynchronous linear pipeline (ALP) and the performance of the ALP as buffers are allocated. Then the relationship is used in order to characterize a local optimum situation on the buffer design space of the ALP. Using the characterization we propose an efficient algorithm optimizing buffer allocation on an ALP in order to achieve its average case performance. Without the loss of optimality, our algorithm works in linear time complexity so it achieves fast buffer-configuration optimization. This paper makes two contributions. First, it describes relationship between the performance characteristics of an ALP and a local optimum on the buffer design space of the ALP. Second, it devises a buffer allocation algorithm finding an optimum solution in linear time complexity.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.5
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• pp.205-210
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• 2007

The integration method of binary and real encoding in genetic algorithm is proposed to deal with design variables of various types in gear drive design. The method is applied to optimum design of multi-stage gear drive. Integer and Discrete type design variables represent the number of teeth and module, and continuous type design variables represent face width, helix angle and addendum modification factor etc. The proposed genetic algorithm is applied for the gear ratio optimization and the volume optimization(minimization) of multi-stage geared motor which is used in field. In result, the proposed design optimization method shows an effectiveness in optimum design process and the new design has a better results compared with the existing design.

• Steel and Composite Structures
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• v.25 no.4
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• pp.473-484
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• 2017

In this study a seismic retrofit scheme for a building structure was presented using steel plate slit dampers. The energy dissipation capacity of the slit damper used in the retrofit was verified by cyclic loading test. Genetic algorithm was applied to find out the optimum locations of the slit dampers satisfying the target displacement. The seismic retrofit of the model structure using the slit dampers was compared with the retrofit with enlarging shear walls. A simple damper distribution method was proposed using the capacity spectrum method along with the damper distribution pattern proportional to the inter-story drifts. The validity of the simple story-wise damper distribution procedure was verified by comparing the results of genetic algorithm. It was observed that the capacity-spectrum method combined with the simple damper distribution pattern leaded to satisfactory story-wise distribution of dampers compatible with the optimum solution obtained from genetic algorithm.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.797-802
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• 2000

Flow shop scheduling problem involves processing several jobs on common facilities where a setup time Is incurred whenever there is a switch of jobs. Practical aspect of scheduling focuses on finding a near-optimum solution within a feasible time rather than striving for a global optimum. In this paper, a hybrid meta-heuristic method called tabu-genetic algorithm(TGA) is suggested, which combines the genetic algorithm(GA) with tabu list. The experiment shows that the proposed TGA can reach the optimum solution with higher probability than GA or SA(Simulated Annealing) in less time than TS(Tabu Search). It also shows that consideration of setup time becomes more important as the ratio of setup time to processing time increases.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.195-202
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• 1995

This paper presents the calculating method of optimum correction mass within permissible vibration limits for rotating machinery in two-plane field balancing. Basic technique of this method based on influence coefficient method, is graphic vector composition that the resultant of two influence vectors obtained by trial mass have to be equilibrium with initial vibration vector in the each correction plane. Genetic algorithm which is a search algorithm based on the mechanics of natural selection and natural genetics is used for vector composition, and SUMT method is used to objective function which seeks optimum correction mass for balancing a rotor.