• Journal of the Korean Geotechnical Society
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• v.20 no.5
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• pp.37-48
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• 2004

This study is focused on the constitutive model in order to represent brittleness and dilatancy for cohesionless soils. The constitutive model was based on an anisotropic hardening rule derived from generalized isotropic hardening nile, which includes an appropriate hardening equation for the overall strain behavior at small to large strains. The yield surface is a simple cylinder type in stress space and it makes the model practically useful. Hence dilatancy behavior in cohesionless soils could be modeled reasonably. A peak stress ratio was defined in order to model brittle stress-strain relationships. An optimized design methodology was proposed on the basis of real-coded genetic algorithm in order to determine parameters for the proposed model systematically. The material parameters were then determined by that algorithm. In order to verify the proposed model, triaxial tests were performed under $K_0$ conditions far weathered soils. In comparison with the triaxial test results under $K_0$ conditions, the proposed model could calculate appropriately the actual effective stress behavior on brittle stress-strain relationships and dilatancy.

• Advances in Energy Research
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• v.4 no.2
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• pp.163-176
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• 2016

Rapid depletion of fossil based oil, coal and gas reserves and its greater demand day by day necessitates the search for other alternatives. Severe environmental impacts caused by the fossil fire based power plants and the escalating fuel costs are the major challenges faced by the electricity supply industry. Integration of Distributed Generators (DG) especially, wind and solar systems to the grid has been steadily increasing due to the concern of clean environment. This paper focuses on a new simple and fast load flow algorithm named Backward Forward Sweep Algorithm (BFSA) for finding the voltage profile and power losses with the integration of various sizes of DG at different locations. Genetic Algorithm (GA) based BFSA is adopted in finding the optimal location and sizing of DG to attain an improved voltage profile and considerable reduced power loss. Simulation results show that the proposed algorithm is more efficient in finding the optimal location and sizing of DG in 15-bus radial distribution system (RDS).The authenticity of the placement of optimized DG is assured with other DG placement techniques.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.77-80
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• 1998

The theoretical foundations of simple genetic algorithm(SGA) are the Schema Theorem and the Building Block Hypothesis. Although SGA does well in many applications as an optimization method, still it does not guarantee the convergence of a global optimum in GA-hard problems and deceptive problems. Therefore as an alternative scheme, there is a growing interest in a co-evolutionary system, where two populations constantly interact and cooperate each other. In this paper we show why the co-evolutionary algorithm works better than SGA in terms of an extended schema theorem. Also the experimental results show a co-evolutionary algorithm works well in optimization problems.

• Journal of the Korea Society of Computer and Information
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• v.17 no.9
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• pp.139-147
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• 2012

This paper suggests simple search algorithm for optimal solution in assignment problem. Generally, the optimal solution of assignment problem can be obtained by Hungarian algorithm. The proposed algorithm reduces the 4 steps of Hungarian algorithm to 1 step, and only selects the minimum cost of row and column then gets the optimal solution simply. For the 27 balanced and 7 unbalanced assignment problems, this algorithm finds the optimal solution but the genetic algorithm fails to find this values. This algorithm improves the time complexity O($n^3$) of Hungarian algorithm to O(n). Therefore, the proposed algorithm can be general algorithm for assignment problem replace Hungarian algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.3
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• pp.17-23
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• 2008

In this research article, scheduling a casting sequence in a job-shop type foundry involving a variety of casts made of an identical alloy but with different shapes and II weights, has been investigated. The objective is to produce the assigned mixed orders satisfying due dates and obtaining the highest ingot efficiency simultaneously. Implementing simple integer programming instead of complicated genetic algorithms accompanying rigorous calculations proves that it can provide a feasible solution with a high accuracy for a complex, multi-variable and multi-constraint optimization problem. Enhancing the ingot efficiency under the constraint of discrete ingot sizes is accomplished by using a simple and intelligible algorithm in a standard integer programming. Employing this simple methodology, a job-shop type foundry is able to maximize the furnace utilization and minimize ingot waste.

• Journal of Korean Society of Steel Construction
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• v.17 no.2 s.75
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• pp.173-181
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• 2005

This study deals with a method to identify damages of free vibrating thin plate structures using the combined finite element method (FEM) and the advanced uniform micro-genetic algorithm.To solve the inverse problem using the combined method, this study uses several natural frequencies instead of mode shapes in a structure as the measured data. The technique described in this paper allows us not only to detect the damaged elements but also to find their numbers, locations, and the extent of damage.To demonstrate the feasibility of the proposed method, the algorithm is applied to a free vibrating steel thin plate structures with arbitrary damages. From the standpoint of computation efficiency, the proposed method in this study has advantages when compared with the existing simple genetic algorithms. The numerical examples demonstrate that the method using micro-genetic algorithms can possibly detect correctly the damages of thin plates from only several natural frequencies instead of their natural modes.

• Fire Science and Engineering
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• v.24 no.2
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• pp.106-113
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• 2010

Fire characteristics can be analyzed more realistically by using more accurate material properties related to the fire dynamics and one way to acquire these fire properties is to use one of the inverse property analyses. In this study the genetic algorithm which is frequently applied for the inverse heat transfer problems is selected to demonstrate the procedure of obtaining fire properties of the solid charring material with relatively simple chemical structure. The thermal decomposition on the surface of the test plate is occurred by receiving the radiative energy from external heat sources, and in this process the heat transfer through the test plate can be simplified by an unsteady 1-D problem. The inverse property analysis based on the genetic algorithm is then applied for the estimation of the properties related to the reaction pyrolysis. The input parameters for the analysis are the surface temperature and mass loss rate of the char plate which are determined from the unsteady 1-D analysis with a givenset of 8 properties. The estimated properties using the inverse analysis based on the genetic algorithm show acceptable agreements with the input properties used to obtain the surface temperature and mass loss rate with errors between 1.8% for the specific heat of the virgin material and 151% for the specific heat of the charred material.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.10a
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• pp.229-230
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• 2006

• Proceedings of the KIEE Conference
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• 2001.07e
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• pp.17-19
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• 2001

In this paper, we constitute H/W systems for the accurate control of DC servo motor. This H/W systems are designed by applying a simple genetic algorithm (SGA) to the robust $H_{\infty}$ control system and the intelligent Fuzzy control system of DC motor, respectively. To verify the effectiveness of the proposed systems, the characteristics of this systems are analysed and simulated.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.53-58
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• 2002

In the construction of successful fuzzy models for nonlinear systems, the identification of an optimal fuzzy model system is an important and difficult problem. Traditionally, sGA(simple genetic algorithm) has been used to identify structures and parameters of fuzzy model because it has the ability to search the optimal solution somewhat globally. But SGA optimization process may be the reason of the premature local convergence when the appearance of the superior individual at the population evolution. Therefore, in this paper we propose a new method that can yield a successful fuzzy model using VmGA(virus messy genetic algorithms). The proposed method not only can be the countermeasure of premature convergence through the local information changed in population, but also has more effective and adaptive structure with respect to using changeable length string. In order to demonstrate the superiority and generality of the fuzzy modeling using VmGA, we finally applied the proposed fuzzy modeling methodof a complex nonlinear system.