• Journal of the Korea Society of Computer and Information
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• v.16 no.7
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• pp.13-23
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• 2011

Grid computing is a new generation computing technology which organizes virtual high-performance computing system by connecting and sharing geographically distributed heterogeneous resources, and performing large-scaled computing operations. In order to maximize the performance of grid computing, job scheduling is essential which allocates jobs to resources effectively. Many studies have been performed which minimize total completion times, etc. However, resource costs are also important, and through the minimization of resource costs, the overall performance of grid computing and economic efficiency will be improved. So in this paper, we propose a multi-objective job scheduling model considering both time and cost. This model derives from the optimal scheduling solution using NSGA-II, which is a multi objective genetic algorithm, and guarantees the effectiveness of the proposed model by executing experiments with those of existing scheduling models such as Min-Min and Max-Min models. Through experiments, we prove that the proposed scheduling model minimizes time and cost more efficiently than existing scheduling models.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.7
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• pp.673-679
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• 2015

In this study, the nondominated sorting genetic algorithm (NSGA-II) is used to obtain the optimized proportional-integral-derivative (PID) gain value that can quickly recover the motion of a quadcopter after a disturbance. Prior to PID control, the four-rotor quadcopter interval was defined using computational fluid dynamics (CFD). Through the definition of this model, the PID control algorithm was generated. To construct a response surface model, D-optimal programming was used for the generation of experimental points. For this purpose, a gain value that satisfies both the roll and altitude PID gain values is obtained. Using the NSGA-II, the gain value of shorten time of the quadcopter motion control can be optimized.

• Journal of the Korea Society for Simulation
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• v.19 no.3
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• pp.55-62
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• 2010

This paper studies the problem seeking an efficient operational scheduling for battle ships in the Republic of Korea's navy. The ships' availability means that their main systems such as weapons, navigation and propulsion are in full operational readiness. If some of the major systems are not ready, then the ships should not be available for operations. It is required to maintain a high level availability under the limited resources as it determines the strength of ROK's navy. However, it will result in inefficiencies if some ships are operated without proper maintenance only to improve their availability. Thus, this study suggests the operational scheduling for two squadron ships that considers multiple objectives such as availability, overlapping maintenance, and deviation from available ships in a particular week. We applied NSGA-II algorithm to find better solutions for more efficient scheduling. The experiment result reached an efficient solutions after 1,500 generations. Two efficient operational schedules were compared on the basis of three multiple objectives among them.

• Journal of Korea Water Resources Association
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• v.40 no.9
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• pp.677-685
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• 2007

The objective of this study is to evaluate the applicability of multi-objective genetic algorithm(MOGA) in order to calibrate the parameters of conceptual rainfall-runoff model, Tank model. NSGA-II, one of the most imitating MOGA implementations, is combined with Tank model and four multi-objective functions such as to minimize volume error, root mean square error (RMSE), high flow RMSE, and low flow RMSE are used. When NSGA-II is employed with more than three multi-objective functions, a number of Pareto-optimal solutions usually becomes too large. Therefore, selecting several preferred Pareto-optimal solutions is essential for stakeholder, and preference-ordering approach is used in this study for the sake of getting the best preferred Pareto-optimal solutions. Sensitivity analysis is performed to examine the effect of initial genetic parameters, which are generation number and Population size, to the performance of NSGA-II for searching the proper paramters for Tank model, and the result suggests that the generation number is 900 and the population size is 1000 for this study.

• Journal of Ocean Engineering and Technology
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• v.27 no.2
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• pp.27-32
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• 2013

A valve is a marine structure that is subjected to multiple seawater loads. Therefore, it is necessary to define the kind of loads applied to it to confirm whether the structure has sufficient strength. In this research, we aimed to find the optimal solution for the stress and deformation of valves under various loads. We first selected design variables and implement a finite element analysis according to changes in the thickness of each component of a valve based on a central composite design. Next we developed a regression model of the response surface. Using this model, we calculated the optimal objective value based on NSGA-II. Finally, to confirm the correspondence between the optimal objective value and the real FEM value, we compared the optimal result and structural analysis result to verify the performance of NSGA-II.

• Journal of Advanced Marine Engineering and Technology
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• v.37 no.8
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• pp.893-904
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• 2013

In this paper, a Kriging metamodel-based multi-objective optimization strategy in conjunction with an NSGA-II(non-dominated sorted genetic algorithm-II) has been employed to optimize the valve-plate shape of the axial piston pump utilizing 3D CFD simulations. The optimization process for minimum pressure ripple and maximum pump efficiency is composed of two steps; (1) CFD simulation of the piston pump operation with various combination of six parameters selected based on the optimization principle, and (2) applying a multi-objective optimization approach based on the NSGA-II using the CFD data set to evaluate the Pareto front. Our exploration shows that we can choose an optimal trade-off solution combination to reach a target efficiency of the axial piston pump with minimum pressure ripple.

• 한국전산유체공학회:학술대회논문집
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• 2008.03b
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• pp.113-116
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• 2008

This work presents numerical optimization for design of staggered arrays of dimples printed on opposite surfaces of a cooling channel with a fast and elitist Non-Dominated Sorting of Genetic Algorithm (NSGA-II) of multi-objective optimization. As Pareto optimal front produces a set of optimal solutions, the trends of objective functions with design variables are predicted by hybrid multi-objective evolutionary algorithm. The problem is defined by three non-dimensional geometric design variables composed of dimpled channel height, dimple print diameter, dimple spacing and dimple depth to maximize heat transfer rate compromising with pressure drop. Twenty designs generated by Latin hypercube sampling were evaluated by Reynolds-averaged Navier-Stokes solver and the evaluated objectives were used to construct Pareto optimal front through hybrid multi-objective evolutionary algorithm. The optimum designs were grouped by k-mean clustering technique and some of the clustered points were evaluated by flow analysis. With increase in dimple depth, heat transfer rate increases and at the same time pressure drop also increases, while opposite behavior is obtained for the dimple spacing. The heat transfer performance is related to the vertical motion of the flow and the reattachment length in the dimple.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.3
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• pp.302-307
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• 2015

The automobile is an important means of transportation. For this reason, the automotive wheel is also an important component in the automotive industry because it acts as a load support and is closely related to safety. Thus, the wheel design is a very important safety aspect. In this paper, an optimal design for minimizing automotive wheel stress and increasing wheel safety is described. To study the optimal design, a central composite design (CCD) and D-optimal design theory are applied, and the approximate function using the response surface method (RSM) is generated. The optimal solutions using the non-dominant sorting genetic algorithm (NSGA-II) are then derived. Comparing CCD and D-optimal solution accuracy and verified the CCD can deduce more accuracy optimal solutions.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.24 no.2
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• pp.192-197
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• 2015

In many fields, the importance of reducing weight is increasing. A product should be designed such that it is profitable, by lowering costs and exhibiting better performance than other similar products. In this study, the mass and deflection of steel structures have to be reduced as objective functions under constraint conditions. To reduce computational analysis time, central composite design(CCD) and D-Optimal are used in design of experiments(DOE). The accuracy of approximate models is evaluated using the $R^2$ value. In this study, the objective functions are multiple, so the non-dominant sorting genetic algorithm(NSGA-II), which is highly efficient, is used for such a problem. In order to verify the validity of Pareto solutions, CAE results and Pareto solutions are compared.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.26 no.1
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• pp.30-35
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• 2017

The major parts of a vacuum cleaner are molded. The vacuum cleaner works in multi-load conditions. Therefore, the designer needs to optimize the structure and injection molding conditions simultaneously. Here, the main factor of design is the rib shape and thickness. The greater the rib thickness, the greater the stiffness of the structure. However, it causes an increase in weight. On the other hand, the lower the rib thickness, the greater the increase in the injection pressure. However, the weight will be reduced. Therefore, the designer needs to optimize the rib shape and thickness for structure stiffness and injection molding. In order to solve this problem, we propose an optimization method using D.O.E and a response surface model, which is a multi-objective optimization method using the multi-objective genetic algorithm.