• Journal of Korea Water Resources Association
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• v.41 no.1
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• pp.1-15
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• 2008

The purpose of this study is to propose a procedure that will be able to find the most efficient sets of weight coefficients for the Geum-River basin in Korea. The result obtained from multi-objective optimization model is inherently sensitive to the weight coefficient on each objective. In multi-objective reservoir operation problems, the coefficient setting may be more complicated because of the natural variation of inflow. Therefore, for multi-objective reservoir operation problems, it may be important for modelers to provide reservoir operators with appropriate sets of weight coefficients considering the inflow variation. This study presents a procedure to find an appropriate set of weight coefficients under the situation that has inflow variation. The proposed procedure uses GA-CoMOM to provide a set of weight coefficient sets. A DEA-window analysis and a cross efficiency analysis are then performed in order to evaluate and rank the sets of weight coefficients for various inflow scenarios. This proposed procedure might be able to find the most efficient sets of weight coefficients for the Geum-River basin in Korea.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.46-51
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• 2014

The most important process of engineering system optimal design is to identify the relationship between the design variables and system response. In case of the system optimization, Response Surface Method (RSM) is widely used. The optimization process of RSM generates the design space using the typical alternative candidates and finds the optimal design point in the generated design space. By changing the optimal point depending on the configuration of the design space, it is important to generate the design space. Therefor in this study, the design space is generated by using the relationship between design variables and system response based on Neuro-Response Surface Method (NRSM). And I try to construct the framework for optimal shape design based on NRSM that the optimum shape can be predicted using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) within the generated design space. In order to verify the usefulness of the constructed framework, we applied the nonlinear mathematical function problem. In this study, we can solve the constraints of time in the optimization process for the engineering problem and effective to determine the optimal design was possible. by using the generated framework for optimal shape design based on NRSM. In the future research, we try to apply the optimization problem for Naval Architectural & Ocean Engineering based on the results of this study.

• Journal of Intelligence and Information Systems
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• v.12 no.2
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• pp.125-144
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• 2006

The development of mobile applications is fast in recent years. However, nearly all applications are for messaging, financial, locating services based on simple interactions with mobile users because of the limited screen size, narrow network bandwidth, and low computing power. Processing an algorithm for supporting a group decision process on mobile devices becomes impossible. In this paper, we introduce the mobile-oriented simple interactive procedure for support a group decision making process. The interactive procedure is developed for multiple objective linear programming problems to help the group select a compromising solution in the mobile Internet environment. Our procedure lessens the burden of group decision makers, which is one of necessary conditions of the mobile environment. Only the partial weak order preferences of variables and objectives from group decision makers are enough for searching the best compromising solution. The methodology is designed to avoid any assumption about the shape or existence of the decision makers' utility function. For the purpose of the experimental study of the procedure, we developed a group decision support system in the mobile Internet environment, MOBIGSS and applied to an allocation problem of investor assets.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1D
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• pp.107-117
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• 2008

Private financing is playing an increasing role in public infrastructure construction projects worldwide. However, private investors/operators are exposed to the financial risk of low profitability due to the inaccurate estimation of facility demand, operation income, maintenance costs, etc. From the operator's perspective, a sound and thorough financial feasibility study is required to establish the appropriate capital structure of a project. Operators tend to reduce the equity amount to minimize the level of risk exposure, while creditors persist to raise it, in an attempt to secure a sufficient level of financial involvement from the operators. Therefore, it is important for creditors and operators to reach an agreement for a balanced capital structure that synthetically considers both profitability and repayment capacity. This paper presents an optimal capital structure model for successful private infrastructure investment. This model finds the optimized point where the profitability is balanced with the repayment capacity, with the use of the concept of utility function and multi-objective GA (Generic Algorithm)-based optimization. A case study is presented to show the validity of the model and its verification. The research conclusions provide a proper capital structure for privately-financed infrastructure projects through a proposed multi-objective model.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.6
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• pp.411-418
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• 2020

In this paper, approximation and optimization methods are proposed for the structural performance of the composite fan blade. Using these methods, we perform the optimal design of the stacking sequence to maximize stiffnesses without changing the mass and the geometric shape of the composite fan blade. In this study, the lamination parameters are introduced to reduce the design variables and space. From the characteristics of lamination parameters, we generate response surface model having a high fitness value. Considering the requirements of the optimal stacking sequence, the multi-objective optimization problem is formulated. We apply the two-step optimization method that combines gradient-based method and genetic algorithm for efficient search of an optimal solution. Finally, the finite element analysis results of the initial and the optimized model are compared to validate the approximation and optimization methods based on the lamination parameters.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2000-2007
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• 1999

Recently, DDS (Distributed Database System) has been often implemented on VAN (Value Added Network) as we know the amazing expansion of information network. DDS can yield significant cost and response time advantages over centrailzed systems for geographically distributed organizations. However, inappropriate design can result in high cost and poor response time. In a DDS design, the main problem is 1) how to select proper computer, and 2) how to allocate data fragment into proper nodes. This paper addresses DDS design problem of selecting the proper class of computers and the allocating data files on VAN. Also, the formulated model includes tow objectives, the operating and investment cost. GA (Genetic Algorithm) is developed to solve this mathematical formulation. A numerical experiment shows that the proposed method arrives at a good solution.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.375-381
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• 2005

Usually genetic algorithms are used to design optimal system. However the performance of the algorithm is determined by the fitness function and the system environment. It is expected that a co-evolutionary algorithm, two populations are constantly interact and co-evolve, is one of the solution to overcome these problems. In this paper we propose three types of co-evolutionary algorithm to solve GA-Hard problem. The first model is a competitive co-evolutionary algorithm that solution and environment are competitively co-evolve. This model can prevent the solution from falling in local optima because the environment are also evolve according to the evolution of the solution. The second algorithm is schema co-evolutionary algorithm that has host population and parasite (schema) population. Schema population supply good schema to host population in this algorithm. The third is game model-based co-evolutionary algorithm that two populations are co-evolve through game. Each algorithm is applied to visual servoing, robot navigation, and multi-objective optimization problem to verify the effectiveness of the proposed algorithms.

• Journal of the Society of Naval Architects of Korea
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• v.33 no.1
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• pp.161-172
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• 1996

Although conventional computer programs use efficient and precise optimization algorithms, they can not emulate the problem solving capabilities of human experts. A design optimization process involves a number of tasks which require human expertise and experience. Traditional optimization systems have concentrated on numerical aspects of a design process and have not been successful in integrating the numerical parts with human expertise. On the other hand, most knowledge-based systems focus on symbolic reasoning and have been little concerned with the numerical processes. The objective of this paper is to develop a knowledge-based multiobjective optimum design system which has the capabilities of knowledge processing and numerical computation by integrating the multiobjective optimization method and the knowledge-based system. The knowledge-based system for symbolic processing is developed. Rules for knowledge representation and the inference mechanism of the system are written in LISP. The knowledge-based multiobjective optimum design system is finally developed by integrating the multiobjective optimization method and the knowledge-based system by applying shell programming technique. The system is applied to an optimum design model of a LNG carrier in the preliminary design stage. It is found that the system well simulate design variables and objective functions of the design model.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.1 no.1
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• pp.83-90
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• 2000

DEA is a newly developed analyzing tool to measure efficiency evaluation of decision making units (DMU). It compares DMU by radial Projection on the efficient frontier. The purpose of this study is to show reference point approach used for searching solution in multiple objective linear Programming can be usefully used to determine flexible efficient frontier of each DMU In reference point approach, the minimization of ASF Produces an efficient points in frontier and enhances the usefulness of DEA by Providing flexibility in DEA and optimally allocating resources to DMU. Various DEA models can be supported by reference point method by changing the projection direction in order to choose the targets units, standards costs and management benching-marking.

• Journal of the Korea Computer Industry Society
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• v.6 no.3
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• pp.539-550
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• 2005

The spanning tree, which is being used the most widely in indoor wiring network, is chosen for the network topology of the optimal LAN design. To apply a spanning tree to GA, the concept of $Pr\ddot{u}fer$ numbers is used. $Pr\ddot{u}fer$ numbers can express he spanning tree in an efficient and brief way, and also can properly represent the characteristics of spanning trees. This paper uses Pareto Stratum-Niche Cubicle(PS-NC) GA by complementing the defect of the same priority allowance in non-dominated solutions of pareto genetic algorithm(PGA). By applying the PS-NC GA to the LAN design areas, the optimal LAN topology design in terms of minimizing both message delay time and connection-cost could be accomplished in a relatively short time. Numerical analysis has been done for a hypothetical data set. The results show that the proposed algorithm could provide better or good solutions for the multi-objective LAN design problem in a fairly short time.