• International Journal of Naval Architecture and Ocean Engineering
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• v.9 no.3
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• pp.246-255
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• 2017

The proposed design methodology represents a new approach to optimize the propeller-hull system simultaneously. In this paper, two objective functions are considered, the first objective function is Lifetime Fuel Consumption (LFC) and the other one is cost function including thrust, torque, open water and skew efficiencies. The variables of the propeller geometries (Z, EAR, P/D and D) and ship hull parameters (L/B, B/T, T and $C_B$) are considered to be optimized with cavitation, blades stress of propeller. The well-known evolutionary algorithm based on NSGA-II is employed to optimize a multi-objective problem, where the main propeller and hull dimensions are considered as design variables. The results are presented for a series 60 ship with B-series propeller. The results showed that the proposed method is an appropriate and effective approach for simultaneously propeller-hull system design and is able to minimize both of the objective functions significantly.

• Steel and Composite Structures
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• v.25 no.4
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• pp.403-413
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• 2017

The fabric braided braking hose that delivers the driver's braking force to brake cylinder undergoes the large deformation cyclic motion according to the steering and bump/rebound motions of vehicle. The cyclic large deformation of braking hose may give rise to two critical problems: the interference with other adjacent vehicle parts and the micro cracking stemming from the fatigue damage accumulation. Hence, both the hose deformation and the fatigue damage become the critical issue in the design of braking hose. In this context, this paper introduces a multi-objective optimization method for minimizing the both quantities. The total length of hose and the helix angles of fabric braided composite layers are chosen for the design variables, and the maximum hose deformation and the critical fatigue life cycle are defined by the individual single objective functions. The trade-off between two single objective functions is made by introducing the weighting factors. The proposed optimization method is validated and the improvement of initial hose design is examined through the benchmark simulation. Furthermore, the dependence of optimum solutions on the weighting factors is also investigated.

• Structural Engineering and Mechanics
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• v.68 no.5
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• pp.633-638
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• 2018

One of the important problems in the vehicle design is vehicle handling and stability. Effective parameters which should be considered in the vehicle handling and stability are roll angle, camber angle and scrub radius. In this paper, a planar vehicle model is considered that two right and left suspensions are double wishbone suspension system. For a better analysis of the suspension geometry, a kinestatic model of vehicle is considered which instantaneous kinematic and statics relations are analyzed simultaneously. In this model, suspension geometry is considered completely. In order to optimum design of double wishbones suspension system, a multi-objective genetic algorithm is applied. Three important parameters of suspension including roll angle, camber angle and scrub radius are taken into account as objective functions. Coordinates of suspension hard points are design variables of optimization which optimum values of them, corresponding to each optimum point, are obtained in the optimization process. Pareto solutions for three objective functions are derived. There are important optimum points in these Pareto solutions which each point represents an optimum status in the model. In other words, corresponding to any optimal point, a specific geometric position is determined for the suspension hard points. Each of the obtained points in the Pareto optimization can be selected for a special design purpose by designer to create an optimum condition in the vehicle handling and stability.

• Communications of the Korean Mathematical Society
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• v.25 no.1
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• pp.139-147
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• 2010

A generalized nondifferentiable fractional optimization problem (GFP), which consists of a maximum objective function defined by finite fractional functions with differentiable functions and support functions, and a constraint set defined by differentiable functions, is considered. Recently, Kim et al. [Journal of Optimization Theory and Applications 129 (2006), no. 1, 131-146] proved optimality theorems and duality theorems for a nondifferentiable multiobjective fractional programming problem (MFP), which consists of a vector-valued function whose components are fractional functions with differentiable functions and support functions, and a constraint set defined by differentiable functions. In fact if $\overline{x}$ is a solution of (GFP), then $\overline{x}$ is a weakly efficient solution of (MFP), but the converse may not be true. So, it seems to be not trivial that we apply the approach of Kim et al. to (GFP). However, modifying their approach, we obtain optimality conditions and duality results for (GFP).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.3
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• pp.369-374
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• 2008

This paper presents the evaluation procedures and the estimation method for the estimation of optimal rebate level for EE(Energy Efficiency) programs. The penetration amount of each appliance is estimated by applying price function to preferred diffusion model resulted from model compatibility test. To estimate the optimal rebate level, two objective functions which express the maximum energy saving and operation benefit are introduced and by multi-objective function which can simultaneously consider two objective functions the optimal rebate level of each appliance is estimated. And then, using the decided rebate level and each penetration amount, the priority order for reasonable investment of each high-efficiency appliance is estimated compared to the results of conventional method. Finally, using a benefit/cost analysis based on California standard practice manual, the economic analysis is implemented for the four perspectives such as participant, ratepayer impact measure, program administrator cost and total resource cost.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.231-236
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• 2001

The goal of multiphase optimization of machine structure is to obtain 1) light weight, 2) statically and dynamically rigid structure. The entire optimization process is carried out in two phases. In the first phase, multiple optimization problem with two objective functions is treated using pareto genetic algorithm. Two objective functions are weight of the structure, and static compliance. In the second phase, maximum receptance is minimized using genetic algorithm. The method is applied to design of quill type machine structure with back column.

• Journal of the Korean Operations Research and Management Science Society
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• v.6 no.1
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• pp.5-13
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• 1981

This paper considers a facility moving along a route of straight line to serve several facilities in xy plane. A route is obtained which minimizes the objective function expressed in terms of average rectilinear distance between the service facility and facilities to he served. The forms of the objective functions can be classified into several cases according to the initial and end points of the routes. In some cases, as in the problem of optimal location of new facility, the cordinates of the optimal routes are median of the cordinates of facilities to be served. In others, the optimal routes are obtained after examining the values of objective functions on the boundaries of slopes and intercepts. An overall optimal route is obtained by comparing these routes. The entire procedure has been programmed in BASIC language, and several case examples are given.

• 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.

• Structural Engineering and Mechanics
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• v.44 no.3
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• pp.325-337
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• 2012

In this article, the multi-objective optimization of cylindrical aluminum tubes under axial impact load is presented. The specific absorbed energy and the maximum crushing force are considered as objective functions. The geometric dimensions of tubes including diameter, length and thickness are chosen as design variables. D/t and L/D ratios are constricted in the range of which collapsing of tubes occurs in concertina or diamond mode. The Non-dominated Sorting Genetic Algorithm-II is applied to obtain the Pareto optimal solutions. A back-propagation neural network is constructed as the surrogate model to formulate the mapping between the design variables and the objective functions. The finite element software ABAQUS/Explicit is used to generate the training and test sets for the artificial neural networks. To validate the results of finite element model, several impact tests are carried out using drop hammer testing machine.

• International Journal of Automotive Technology
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• v.8 no.5
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• pp.563-573
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• 2007

Optimal design of an automotive catalytic converter for minimization of cold-start emissions is numerically performed using a micro genetic algorithm for two optimization problems: optimal geometry design of the monolith for various operating conditions and optimal axial catalyst distribution. The optimal design process considered in this study consists of three modules: analysis, optimization, and control. The analysis module is used to evaluate the objective functions with a one-dimensional single channel model and the Romberg integration method. It obtains new design variables from the control module, produces the CO cumulative emissions and the integral value of a catalyst distribution function over the monolith volume, and provides objective function values to the control module. The optimal design variables for minimizing the objective functions are determined by the optimization module using a micro genetic algorithm. The control module manages the optimal design process that mainly takes place in both the analysis and optimization modules.