• Structural Engineering and Mechanics
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• v.81 no.2
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• pp.195-203
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• 2022

This paper presents the quantification and location damage detection of plane and space truss structures in a two-phase method to reduce the computations efforts significantly. In the first phase, a proposed damage indicator based on the residual force vector concept is used to get the suspected damaged members. In the second phase, using damage quantification as a variable, a teaching-learning based optimization algorithm (TLBO) is used to obtain the damage quantification value of the suspected members obtained in the first phase. TLBO is a relatively modern algorithm that has proved distinguished in solving optimization problems. For more verification of TLBO effeciency, the classical particle swarm optimization (PSO) is used in the second phase to make a comparison between TLBO and PSO algorithms. As it is clear, the first phase reduces the search space in the second phase, leading to considerable reduction in computations efforts. The method is applied on three examples, including plane and space trusses. Results have proved the capability of the proposed method to precisely detect the quantification and location of damage easily with low computational efforts, and the efficiency of TLBO in comparison to the classical PSO.

• Proceeding of EDISON Challenge
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• 2016.03a
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• pp.183-189
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• 2016

In this paper, an optimization study on a helicopter rotor blade cross-section was made. Generalization was made to the baseline cross-section to simplify the analysis. To have better performance in aeroelastic response, with the aerodynamic center being the origin of the baseline, the distance between aerodynamic center and shear center, and the distance between mass center and shear center of the blade were minimized. For efficient searching of optimum solutions over the design space, grid search method, which is a method of graphical search was used. Two design variables, radius of balancing weight at leading edge, and offset of the spar from leading edge were selected for the study. Cubic spline interpolation method was used to accommodate searching of the optimum solution. 2-Leveled searching system was devised in accordance with the interpolation method. Optimum solution was found to show 6% decrease in both distance between aerodynamic center and shear center, and mass center and shear center to the baseline.

• 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 KIISE:Software and Applications
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• v.29 no.9
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• pp.645-653
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• 2002

The unit commitment problem in a power system involves determining the start-up and shut-down schedules of many dynamos for a day or a week while satisfying the power demands and diverse constraints of the individual units in the system. It is very difficult to derive an economically optimal schedule due to its huge search space when the number of dynamos involved is large. Tabu search is a popular solution method used for various optimization problems because it is equipped with effective means of searching beyond local optima and also it can naturally incorporate and exploit domain knowledge specific to the target problem. When given a large-scaled problem with a number of complicated constraints, however, tabu search cannot easily find a good solution within a reasonable time. This paper shows that a large- scaled optimization problem such as the unit commitment problem can be solved efficiently by using a parallel tabu search. The parallel tabu search not only reduces the search time significantly but also finds a solution of better quality.

• Korean Journal of Computational Design and Engineering
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• v.15 no.1
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• pp.24-32
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• 2010

This paper introduces a B-spline based branch & bound algorithm for global optimization. The branch & bound is a well-known algorithm paradigm for global optimization, of which key components are the subdivision scheme and the bound calculation scheme. For this, we consider the B-spline hypervolume to approximate an objective function defined in a design space. This model enables us to subdivide the design space, and to compute the upper & lower bound of each subspace where the bound calculation is based on the LHS sampling points. We also describe a search tree to represent the searching process for optimal solution, and explain iteration steps and some conditions necessary to carry out the algorithm. Finally, the performance of the proposed algorithm is examined on some test problems which would cover most difficulties faced in global optimization area. It shows that the proposed algorithm is complete algorithm not using heuristics, provides an approximate global solution within prescribed tolerances, and has the good possibility for large scale NP-hard optimization.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.4
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• pp.207-210
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• 2012

Genetic algorithms(GA) are well known and very popular stochastic optimization algorithm. Although, GA is very powerful method to find the global optimum, it has some drawbacks, for example, premature convergence to local optima, slow convergence speed to global optimum. To enhance the performance of GA, this paper proposes an adaptive partitioning-based genetic algorithm. The partitioning method, which enables GA to find a solution very effectively, adaptively divides the search space into promising sub-spaces to reduce the complexity of optimization. This partitioning method is more effective as the complexity of the search space is increasing. The validity of the proposed method is confirmed by applying it to several bench mark test function examples and the optimization of fuzzy controller for the control of an inverted pendulum.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.8
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• pp.2113-2119
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• 1998

The optimization problem of BDDs plays an improtant role in the area of logic synthesis and formal verification. Since the variable ordering has great impacts on the size and form of BDD, finding a good variable order is very important problem. In this paper, a new variable ordering scheme called incremental optimization algorithm is presented. The proposed algorithm reduces search space more than a half of that of the conventional sifting algorithm, and computing time has been greatly reduced withoug depreciating the performance. Moreover, the incremental optimization algorithm is very simple than other variable reordering algorithms including the sifting algorithm. The proposed algorithm has been implemented and the efficiency has been show using may benchmark circuits.

• Journal of Ocean Engineering and Technology
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• v.17 no.6
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• pp.53-57
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• 2003

Optimum design of most structural system requires that design variables are regarded as discrete quantities. This paper presents the use of Genetic Algorithm for determining the optimum design for truss with discrete variables. Genetic Algorithm are know as heuristic search algorithms, and are effective global search methods for discrete optimization. In this paper, Elitism and the method of conferring penalty parameters in the design variables, in order to achieve improved fitness in the reproduction process, is used in the Genetic Algorithm. A 10-Bar plane truss and a 25-Bar space truss are used for discrete optimization. These structures are designed for stress and displacement constraints, but buckling is not considered. In particular, we obtain continuous solution using Genetic Algorithms for a 10-bar truss, compared with other results. The effectiveness of Genetic Algorithms for global optimization is demonstrated through two truss examples.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.9 no.6
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• pp.21-28
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• 2010

This study shows an optimization procedure for an automatic determination on the gate position to ensure the fill-uniformity within a part cavity by using the injection molding simulation. For an optimization, the maximum pressure-difference within a part cavity induced at the stage of filling is used to evaluate degree of fill-uniformity. In addition, a direct search scheme based on the reduction of design space is developed and applied in the optimization problem. This corresponding proposed methodology was applied in the optimization on the gate location for a CD-tray molding, as a result, showed the improvement of the fill-uniformity within the cavity.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.587-592
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• 2000

The optimization method based on an enhanced genetic algorithms is proposed for multimodal function optimization in this paper. This method is consisted of two main steps. The first step is global search step using the genetic algorithm(GA) and function assurance criterion(FAC). The belonging of an population to initial solution group is decided according to the FAC. The second step is to decide the similarity between individuals, and to research the optimum solutions by simplex method in reconstructive search space. Two numerical examples are also presented in this paper to comparing with conventional methods.