• 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.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.203-211
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• 2022

Multi-objective optimization problems (MOPs) arise in many real-world applications. MOPs involve two or more objectives with the aim to be optimized. With these problems improvement of one objective may led to deterioration of another. The primary goal of most multi-objective evolutionary algorithms (MOEA) is to generate a set of solutions for approximating the whole or part of the Pareto optimal front, which could provide decision makers a good insight to the problem. Over the last decades or so, several different and remarkable multi-objective evolutionary algorithms, have been developed with successful applications. However, MOEAs are still in their infancy. The objective of this research is to study how to use and apply machine learning (ML) to improve evolutionary multi-objective optimization (EMO). The EMO method is the multi-objective evolutionary algorithm based on decomposition (MOEA/D). The MOEA/D has become one of the most widely used algorithmic frameworks in the area of multi-objective evolutionary computation and won has won an international algorithm contest.

• 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.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.6
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• pp.460-467
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• 2019

This paper proposes a multi-objective optimization (MOO) framework for conceptual design of a surface-to-surface missile system. It can generate the set of Pareto optimal system design, which can be used for system trade-off study in a very early stage of the research and development process. The proposed framework consists of four functional modules (an environmental setting module, a variable setting module, a multidisciplinary analysis module and an optimization module) to make the model easy to change, and the concept design process using the framework was able to achieve the purpose of reviewing various designs in the early stage of development. A case study demonstrating the effectiveness of the framework has presented applicability to the system design, and the proposed framework has contributed to presenting a design environment that can ensure reliability and reduce computational time in the conceptual design stage.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.6
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• pp.419-426
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• 2020

The structural design of the steel eccentrically braced frame (EBF) was developed and analyzed in this study through multiobjective optimization (MOO). For the optimal design, NSGA-II which is one of the genetic algorithms was utilized. The amount of structure and interfloor displacement were selected as the objective functions of the MOO. The constraints include strength ratio and rotation angle of the link, which are required by structural standards and have forms of the penalty function such that the values of the objective functions increase drastically when a condition is violated. The regulations in the code provision for the EBF system are based on the concept of capacity design, that is, only the link members are allowed to yield, whereas the remaining members are intended to withstand the member forces within their elastic ranges. However, although the pareto front obtained from MOO satisfies the regulations in the code provision, the actual nonlinear behavior shows that the plastic deformation is concentrated in the link member of a certain story, resulting in the formation of a soft story, which violates the capacity design concept in the design code. To address this problem, another constraint based on the Eurocode was added to ensure that the maximum values of the shear overstrength factors of all links did not exceed 1.25 times the minimum values. When this constraint was added, it was observed that the resulting pareto front complied with both the design regulations and capacity design concept. Ratios of the link length to beam span ranged from 10% to 14%, which was within the category of shear links. The overall design is dominated by the constraint on the link's overstrength factor ratio. Design characteristics required by the design code, such as interstory drift and member strength ratios, were conservatively compared to the allowable values.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.292-296
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• 2010

본 연구에서는 상대적으로 소규모 홍수저감시설인 천변저류지의 설치를 통하여 대규모 유역 하도 전체에서의 홍수위 저감효과를 평가하고 그 효율을 극대화 하는 방안을 제시하였다. 본 연구에 적용한 다목적 최적화 기법(Multi-objective Optimization)으로는 NSGA-II(Non-dominated Sorting Genetic Algorithm II) 알고리즘을 적용하였으며 천변저류지 설치에 따른 수위 영향구간 분석 및 유역 전체 하도구간에서 전반적으로 발생하는 수리, 수문학적인 변화 평가 및 천변저류지 최적 조합을 선정하기 위하여 천변저류지의 용량을 최소화하면서 하도 전 구간에서의 수위 저감량을 최대화할 수 있도록 최적화 알고리즘의 목적함수를 설정하였다. 천변저류지 설치에 따른 홍수량의 변화를 해석하기 위하여 안성천 유역에 대하여 동역학적 홍수추적을 수행하였으며 저류형 구조물의 설치에 따른 홍수량 저감효과 및 그에 따른 홍수위의 변화를 동시에 해석하기 위하여 UNET 모형을 기반으로 한 HEC-RAS 부정류 해석을 실시하였다. 천변저류지 조합별로 다양한 경우의 수가 존재하므로 HEC-RAS 구동 모듈인 HECRAS Controller를 Visual Basic으로 코딩된 최적화 알고리즘 프로그램과 연동함으로써 각 경우의 수별로 동역학적 홍수추적 및 부정류 해석을 실시함으로써 천변저류지 조합별 각 측점에서의 홍수량 및 홍수위를 산정하여 저류지 용량을 최소화하면서 각 하도 측점별 수위저감량을 최대화 하는 최적해 집단(Pareto Front)을 산정하여 제시하였다.

• Steel and Composite Structures
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• v.32 no.4
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• pp.455-466
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• 2019

Despite the rapid advancement in computing resources, many real-life design and optimization problems in structural engineering involve huge computation costs. To counter such challenges, approximate models are often used as surrogates for the highly accurate but time intensive finite element models. In this paper, surrogates for first-order shear deformation based finite element models are built using a polynomial regression approach. Using statistical techniques like Box-Cox transformation and ANOVA, the effectiveness of the surrogates is enhanced. The accuracy of the surrogate models is evaluated using statistical metrics like $R^2$, $R^2{_{adj}}$, $R^2{_{pred}}$ and $Q^2{_{F3}}$. By combining these surrogates with nature-inspired multi-criteria decision-making algorithms, namely multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), the optimal combination of various design variables to simultaneously maximize fundamental frequency and frequency separation is predicted. It is seen that the proposed approach is simple, effective and good at inexpensively producing a host of optimal solutions.

• Transactions of Materials Processing
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• v.30 no.3
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• pp.125-133
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• 2021

In this study, response surface method (RSM), back propagation neural network (BPNN), and genetic algorithm (GA) were used for modeling and multi-objective optimization of the parameters of AA5052-H32 in incremental sheet forming (ISF). The goal of optimization is to determine the maximum forming angle and minimum surface roughness, while varying the production process parameters, such as tool diameter, tool spindle speed, step depth, and tool feed rate. A Box-Behnken experimental design (BBD) was used to develop an RSM model and BPNN model to model the variations in the forming angle and surface roughness based on variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process the GA. The results showed that RSM and BPNN can be effectively used to control the forming angle and surface roughness. The optimized Pareto front produced by the GA can be utilized as a rational design guide for practical applications of AA5052 in the ISF process

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2125-2138
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• 2023

The printed circuit heat exchanger (PCHE) with airfoil fins has the benefits of high compactness, high efficiency and superior heat transfer performance. A novel multi-objective optimization approach is presented to design the airfoil fin PCHE in this paper. Three optimization design variables (the vertical number, the horizontal number and the staggered number) are obtained by means of dimensionless airfoil fin arrangement parameters. And the optimization objective is to maximize the Nusselt number (Nu) and minimize the Fanning friction factor (f). Firstly, in order to investigate the impact of design variables on the thermal-hydraulic performance, a parametric study via the design of experiments is proposed. Subsequently, the relationships between three optimization design variables and two objective functions (Nu and f) are characterized by an improved particle swarm optimization-backpropagation artificial neural network. Finally, a multi-objective optimization is used to construct the Pareto optimal front, in which the non-dominated sorting genetic algorithm II is used. The comprehensive performance is found to be the best when the airfoil fins are completely staggered arrangement. And the best compromise solution based on the TOPSIS method is identified as the optimal solution, which can achieve the requirement of high heat transfer performance and low flow resistance.

• Environmental Engineering Research
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• v.22 no.4
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• pp.407-416
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• 2017

$CO_2$ emission is increasingly focused by public. Beijing and Tianjin are conceived to be a new economic point of growth in China. However, both of them are suffering serious environmental stress. In order to seek for the effect of socioeconomic factors on the $CO_2$ emission of this region, a novel methodology -symbolic regression- is adopted to investigate the relationship between $CO_2$ emission and influential factors of Beijing and Tianjin. Based on this method, $CO_2$ emission models of Beijing and Tianjin are built respectively. The models results manifested that Beijing and Tianjin own different $CO_2$ emission indicators. The RMSE of models in Beijing and Tianjin are 255.39 and 603.99, respectively. Further analysis on indicators and forecast trend shows that $CO_2$ emission of Beijing expresses an inverted-U shaped curve, whilst Tianjin owns a monotonically increasing trend. From analytical results, it could be argued that the diversity rooted in different development orientation and the mixture of different natural and industrial environment. This research further expands the investigation on $CO_2$ emission of Beijing and Tianjin region, and can be used for reference in the study of carbon emissions in similar regions. Based on the investigation, several policy suggestions are presented.