• Wind and Structures
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• v.32 no.4
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• pp.355-369
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• 2021

Shape optimization of tall buildings is an efficient approach to mitigate wind-induced effects. Several studies have demonstrated the potential of shape modifications to improve the building's aerodynamic properties. On the other hand, it is well-known that the cross-section geometry has a direct impact in the floor area availability and subsequently in the building's profitability. Hence, it is of interest for the designers to find the balance between these two design criteria that may require contradictory design strategies. This study proposes a surrogate-based multi-objective optimization framework to tackle this design problem. Closed-form equations provided by the Eurocode are used to obtain the wind-induced responses for several wind directions, seeking to develop an industry-oriented approach. CFD-based surrogates emulate the aerodynamic response of the building cross-section, using as input parameters the cross-section geometry and the wind angle of attack. The definition of the building's modified plan shapes is done adopting the reduced basis approach, advancing the current strategies currently adopted in aerodynamic optimization of civil engineering structures. The multi-objective optimization problem is solved with both the classical weighted Sum Method and the Weighted Min-Max approach, which enables obtaining the complete Pareto front in both convex and non-convex regions. Two application examples are presented in this study to demonstrate the feasibility of the proposed strategy, which permits the identification of Pareto optima from which the designer can choose the most adequate design balancing profitability and occupant comfort.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.1
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• pp.69-74
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• 2007

Topology optimization of the inner reinforcement for a vehicle's hood has been performed by evolutionary structural optimization(ESO) using a smoothing scheme. The purpose of this study is to obtain optimal topology of the inner reinforcement for a vehicle's hood considering the static stiffness of bending and torsion simultaneously. To do this, the multiobjective optimization technique was implemented. Optimal topologies were obtained by the ESO method. From several combinations of weighting factors, a Pareto-optimal solution was obtained. Also, a smoothing scheme was implemented to suppress the checkerboard pattern in the procedure of topology optimization. It is concluded that ESO method with a smoothing scheme is effectively applied to topology optimization of the inner reinforcement of a vehicle's hood considering the static stiffness of bending and torsion.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.520-525
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• 2024

The optimization design problem of nuclear reactor radiation shield is a typical multi-objective optimization problem with almost 10 sub-objectives and the sub-objectives are always demanded to be under tolerable limits. In this paper, a design method combining multi-objective optimization algorithms with paralleling discrete ordinate transportation code is developed and applied to shield design of the Savannah nuclear reactor. Three approaches are studied for light-weighted and compact design of radiation shield. Comparing with directly optimization with 10 objectives and the single-objective optimization, the approach by setting sub-objectives representing weight and volume as optimization objectives while treating other sub-objectives as constraints has the best performance, which is more suitable to reactor shield design.

• Structural Engineering and Mechanics
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• v.23 no.4
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• pp.325-337
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• 2006

A hybrid structural design and optimization methodology that combines the strengths of genetic algorithms, local search techniques, and parallel computing is developed to evolve optimal truss systems in this research effort. The primary objective that is met in evolving near-optimal or optimal structural systems using this approach is the capability of satisfying user-defined design criteria while minimizing the computational time required. The application of genetic algorithms to the design and optimization of truss systems supports conceptual design by facilitating the exploration of new design alternatives. In addition, final shape optimization of the evolved designs is supported through the refinement of member sizes using local search techniques for further improvement. The use of the hybrid approach, therefore, enhances the overall process of structural design. Parallel computing is implemented to reduce the total computation time required to obtain near-optimal designs. The support of human-computer interaction during layout optimization and local optimization is also discussed since it assists in evolving optimal truss systems that better satisfy a user's design requirements and design preferences.

• Clean Technology
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• v.7 no.2
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• pp.99-107
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• 2001

Optimization methodologies have been proposed of find the best environment-friendly recycling pathways of plastic materials based on life-cycle assessment (LCA) methodology. The main difficulty in conducting this optimization study is that multiple environmental burdens have to be considered simultaneously as the cost functions. Instead of generating conservative Pareto or noninferior solutions following multi-objective optimization approaches, we have proposed some practical criteria on how to combine the different environmental burdens into a single measure. The obtained single objective optimization problem can then be solved by conventional nonlinear programming techniques or, more effectively, by a tree search method based on decision flows. The latter method reduces multi-dimensional optimization problems to a set of one-dimensional problems in series. It is expected the suggested tree search approach can be applied to many LCA studies as a new promising optimization tool.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.16 no.5 s.96
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• pp.472-481
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• 2005

This paper reports the design of RFID reader antennas working in UHF band. The reader antennas were designed using a Pareto Genetic Algorithm(Pareto GA). Antennas were optimized to have circular polarization(CP) with less than 3 dB axial ratio, impedance matching with less than VSWR=2 within the frequency range of UHF, an adequate readable range, a restricted size(kr<2.22) considering the practical condition. After Pareto GA optimization, we selected and built the most suitable antenna design and compared the measured results to the simulations. Operating principle of the antenna was explained by investigating the amplitude and the phase of the induced current on the antenna body. We also researched the stability of the antenna with respect to the manufacturing error and studied the critical design parameters by applying the random error method on the antenna bent points.

• Journal of the Korean Society for Railway
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• v.13 no.2
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• pp.222-228
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• 2010

In this paper, we propose a optimization approach for the Electric Locomotive Overhaul Maintenance Facility (ELOMF), which aims at the simulation optimization so as to meet the design specification. In simulation design, we consider the critical path and sensitivity analysis of the critical (dependent) factors and the design (independent) parameters for the parameter selection and reduction of the metamodel. Therefore, we construct the multi-objective non-linear programming. The objective function is normalized for the generalization of design parameter while the constraints are composed of the simulation-based regression metamodel for the critical factors and design factor's domain. Then the effective solution procedure based on the pareto optimal solution set is proposed. This approach provides a comprehensive approach for the optimization of Train Overhaul Maintenance Facility(TOMF)'s design parameters using the simulation and metamoels.

• Management Science and Financial Engineering
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• v.3 no.1
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• pp.75-88
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• 1997

Tabu search (TS) has been successfully applied for solving many complex combinatorial optimization problems in the areas of operations research and production control. However, TS is for single-objective problems in its present form. In this article, a TS-based heuristic is developed to determine Pareto-efficient solutions to a multi-objective combinatorial optimization problem. The developed algorithm is then applied to the discrete optimal design problem in statistics to demonstrate its usefulness.

• International Journal of Fluid Machinery and Systems
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• v.2 no.2
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• pp.179-188
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• 2009

Recent development of high speed computers and use of optimization techniques have given a big momentum of turbomachinery design replacing expensive experimental cost as well as trial and error approaches. The surrogate based optimization techniques being used for aerodynamic turbomachinery designs coupled with Reynolds-averaged Navier-Stokes equations analysis involve single- and multi-objective optimization methods. The objectives commonly tried to improve were adiabatic efficiency, pressure ratio, weight etc. Presently coupling the fluid flow and structural analysis is being tried to find better design in terms of weight, flutter and vibration, and turbine life. The present article reviews the surrogate based optimization techniques used recently in turbomachinery shape optimizations.

• Journal of Korea Water Resources Association
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• v.43 no.12
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• pp.1011-1027
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• 2010

In hydrologic modeling, prediction uncertainty generally stems from various uncertainty sources associated with model structure, data, and parameters, etc. This study aims to assess the parameter uncertainty effect on hydrologic prediction results. For this objective, a distributed rainfall-sediment yield-runoff model, which consists of rainfall-runoff module for simulation of surface and subsurface flows and sediment yield module based on unit stream power theory, was applied to the mesoscale mountainous area (Cheoncheon catchment; 289.9 $km^2$). For parameter uncertainty evaluation, the model was calibrated by a multi-objective optimization algorithm (MOSCEM) with two different objective functions (RMSE and HMLE) and Pareto optimal solutions of each case were then estimated. In Case I, the rainfall-runoff module was calibrated to investigate the effect of parameter uncertainty on hydrograph reproduction whereas in Case II, sediment yield module was calibrated to show the propagation of parameter uncertainty into sedigraph estimation. Additionally, in Case III, all parameters of both modules were simultaneously calibrated in order to take account of prediction uncertainty in rainfall-sediment yield-runoff modeling. The results showed that hydrograph prediction uncertainty of Case I was observed over the low-flow periods while the sedigraph of high-flow periods was sensitive to uncertainty of the sediment yield module parameters in Case II. In Case III, prediction uncertainty ranges of both hydrograph and sedigraph were larger than the other cases. Furthermore, prediction uncertainty in terms of spatial distribution of erosion and deposition drastically varied with the applied model parameters for all cases.