• Steel and Composite Structures
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• v.15 no.4
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• pp.451-466
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• 2013

This paper deals with the multi-objective optimization of tire reinforcement structures such as the tread belt and the carcass path. The multi-objective functions are defined in terms of the discrete-type design variables and approximated by artificial neutral network, and the sensitivity analyses of these functions are replaced with the iterative genetic evolution. The multi-objective optimization algorithm introduced in this paper is not only highly CPU-time-efficient but it can also be applicable to other multi-objective optimization problems in which the objective function, the design variables and the constraints are not continuous but discrete. Through the illustrative numerical experiments, the fiber-reinforced tire belt structure is optimally tailored. The proposed multi-objective optimization algorithm is not limited to the tire reinforcement structure, but it can be applicable to the generalized multi-objective structural optimization problems in various engineering applications.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.656-657
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• 2015

Recently, Multi-Objective Optimization of design elements is an important issue in building design. Design variables that considering the specificities of the different environments should use the appropriate algorithm on optimization process. The purpose of this study is to compare and analyze the optimal solution using three evolutionary algorithms and energy modeling simulation. This paper consists of three steps: i)Developing three evolutionary algorithm model for optimization of design elements ; ii) Conducting Multi-Objective Optimization based on the developed model ; iii) Conducting comparative analysis of the optimal solution from each of the algorithms. Including Non-dominated Sorted Genetic Algorithm (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO) and Random Search were used for optimization. Each algorithm showed similar range of result data. However, the execution speed of the optimization using the algorithm was shown a difference. NSGA-II showed the fastest execution speed. Moreover, the most optimal solution distribution is derived from NSGA-II.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.2 s.245
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• pp.128-134
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• 2006

Topology optimization is an effective scheme to obtain the initial design concept: however, it is hard to apply in case of non-linear or multi-objective problems. In this study, a modified topology optimization method is proposed to generate a structure of a swing arm type actuator satisfying maximum compliance as well. as maximum stiffness using the multi-objective optimization. approach. The multi-objective function is defined to maximize the compliance in the direction of focusing of the actuator and the second eigen-frequency of the structure. The design of experiments are performed and the response surface functions are formulated to construct the multi-objective function. The weighting factors between conflicting functions are determined by the back-error propagation neural network and the solution of multi-objective function is acquired using the genetic algorithm.

• 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.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.10a
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• pp.263-270
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• 2002

This study proposes a multi-objective optimum design method for a rational optimization of high-speed railway bridges. This multi-objective optimization is found to be effective in optimizing multi-objective problems that incorporate cost and dynamic responses such as vertical acceleration and displacement. These design factors are so important in the high-speed railway bridges. And the trade off method which is one of the most typical multi-objective optimization methods is used in this study, since the dynamic factors are formulated as objective function and also considered as constraints. And the Pareto curve can be obtained by performing the multi-objective optimization for real high-speed railway bridges. Thus, it is found that more reasonable design can be obtained when compared with those using conventional design procedure.

• Advances in Computational Design
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• v.1 no.1
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• pp.87-103
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• 2016

This paper adopts and investigates the non-dominated sorting approach for extending the single-objective Cuckoo Search (CS) into a multi-objective framework. The proposed approach uses an archive composed of primary and secondary population to select and keep the non-dominated solutions at each generation instead of pairwise analogy used in the original Multi-objective Cuckoo Search (MOCS). Our simulations show that such a low computational complexity approach can enrich CS to incorporate multi-objective needs instead of considering multiple eggs for cuckoos used in the original MOCS. The proposed MOCS is tested on a set of multi-objective optimization problems and two well-studied engineering design optimization problems. Compared to MOCS and some other available multi-objective algorithms such as NSGA-II, our approach is found to be competitive while benefiting simplicity. Moreover, the proposed approach is simpler and is capable of finding a wide spread of solutions with good coverage and convergence to true Pareto optimal fronts.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2000.04a
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• pp.143-148
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• 2000

In this article, we presents multi-objective design optimization of laminated composite beam using Fuzzy programming method. At first, the two design objectives are minimizing the structural weight and maximizing the buckling load respectively. Fuzzy multi-optimization problem can be formulated based on results of single optimizations. Due to different relative importance of design objectives, membership functions are constructed by adding exponential parameters for different objective's weights. Finite element analysis of composite beam for buckling behavior are carried by Natural mode method proposed by J.Argyris and computational time of analysis can be reduced. With this scheme, a designer can conveniently obtain a compromise optimal solution of a multi-objective optimization problem only by providing some exponential parameters corresponding to the importance of the objective functions.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.3
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• pp.74-81
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• 2019

Herein, we performed a comparative study on approximate multi-objective design optimization, to realize a structural design to improve the weight and vibration performances of the knuckle - a car suspension component - considering various load conditions and vibration characteristics. In the approximate multi-objective optimization process, a regression meta-model was generated using the response surfaces method (RSM), while Kriging and back-propagation neural network (BPN) methods were applied for interpolation meta-modeling. The Pareto solutions, multi-objective optimal solutions, were derived using the non-dominated sorting genetic algorithm (NSGA-II). In terms of the knuckle design considered in this study, the characteristics and influence of the meta-model on multi-objective optimization were reviewed through a comparison of the approximate optimization results with the meta-models and the actual optimization.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.4
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• pp.36-44
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• 2007

This paper is focused on the optimum design for decreasing volume and increasing reliability of multi-stage gear drive. For the optimization on volume and reliability, multi-objective optimization is used. The genetic algorithm is introduced to multi-objective optimization method and it is used to develop the optimum design program using exterior penalty function method to solve the complicated subject conditions. A 5 staged gear drive(geared motor) is chosen to compare the result of developed optimum design method with the existing design. Each of the volume objective, reliability objective, and volume-reliability multi-objectives are performed and compared with existing design. As a result, optimum solutions are produced, which decrease volume and increase reliability. It is shown that the developed design method is good for multi-stage gear drive design.

• 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.