• Title, Summary, Keyword: Multi-objective Function

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Design of a Swing-arm Actuator using the Compliant Mechanism - Multi-objective Optimal Design Considering the Stiffness Effect (컴플라이언트 메커니즘을 이용한 스윙 암 액추에이터의 설계 - 강성 효과를 고려한 다중목적 최적화 설계 -)

  • Lee Choong-yong;Min Seungjae;Yoo Jeonghoon
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.30 no.2
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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.

A novel PSO-based algorithm for structural damage detection using Bayesian multi-sample objective function

  • Chen, Ze-peng;Yu, Ling
    • Structural Engineering and Mechanics
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    • v.63 no.6
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    • pp.825-835
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    • 2017
  • Significant improvements to methodologies on structural damage detection (SDD) have emerged in recent years. However, many methods are related to inversion computation which is prone to be ill-posed or ill-conditioning, leading to low-computing efficiency or inaccurate results. To explore a more accurate solution with satisfactory efficiency, a PSO-INM algorithm, combining particle swarm optimization (PSO) algorithm and an improved Nelder-Mead method (INM), is proposed to solve multi-sample objective function defined based on Bayesian inference in this study. The PSO-based algorithm, as a heuristic algorithm, is reliable to explore solution to SDD problem converted into a constrained optimization problem in mathematics. And the multi-sample objective function provides a stable pattern under different level of noise. Advantages of multi-sample objective function and its superior over traditional objective function are studied. Numerical simulation results of a two-storey frame structure show that the proposed method is sensitive to multi-damage cases. For further confirming accuracy of the proposed method, the ASCE 4-storey benchmark frame structure subjected to single and multiple damage cases is employed. Different kinds of modal identification methods are utilized to extract structural modal data from noise-contaminating acceleration responses. The illustrated results show that the proposed method is efficient to exact locations and extents of induced damages in structures.

Development of Pareto strategy multi-objective function method for the optimum design of ship structures

  • Na, Seung-Soo;Karr, Dale G.
    • International Journal of Naval Architecture and Ocean Engineering
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    • v.8 no.6
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    • pp.602-614
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    • 2016
  • It is necessary to develop an efficient optimization technique to perform optimum designs which have given design spaces, discrete design values and several design goals. As optimization techniques, direct search method and stochastic search method are widely used in designing of ship structures. The merit of the direct search method is to search the optimum points rapidly by considering the search direction, step size and convergence limit. And the merit of the stochastic search method is to obtain the global optimum points well by spreading points randomly entire the design spaces. In this paper, Pareto Strategy (PS) multi-objective function method is developed by considering the search direction based on Pareto optimal points, the step size, the convergence limit and the random number generation. The success points between just before and current Pareto optimal points are considered. PS method can also apply to the single objective function problems, and can consider the discrete design variables such as plate thickness, longitudinal space, web height and web space. The optimum design results are compared with existing Random Search (RS) multi-objective function method and Evolutionary Strategy (ES) multi-objective function method by performing the optimum designs of double bottom structure and double hull tanker which have discrete design values. Its superiority and effectiveness are shown by comparing the optimum results with those of RS method and ES method.

Multi-objective Optimization of Fuzzy System Using Membership Functions Defined by Normed Method (노음방법에 의해 정의된 소속함수를 사용한 퍼지계의 다목적 최적설계)

  • 이준배;이병채
    • Transactions of the Korean Society of Mechanical Engineers
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    • v.17 no.8
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    • pp.1898-1909
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    • 1993
  • In this paper, a convenient scheme for solving multi-objective optimization problems including fuzzy information in both objective functions and constraints is presented. At first, a multi-objective problem is converted into single objective problem based on the norm method, and a merbership function is constructed by selecting its type and providing the parameters defined by the norm method. Finally, this fuzzy programming problem is converted into an ordinary optimization problem which can be solved by usual nonlinear programming techniques. With this scheme, a designer can conveniently obtain pareto optimal solutions of a fuzzy system only by providing some parameters corresponding to the importance of the objectiv functions. Proposed scheme is simple and efficient in treating multi-objective fuzzy systems compared with and method by with membership function value is provided interactively. To show the validity of the scheme, a simple 3-bar truss example and optimal cutting problem are solved, and the results show that the scheme is very useful and easy to treat multi-objective fuzzy systems.

Multi-objective Optimization of a Laidback Fan Shaped Film-Cooling Hole Using Evolutionary Algorithm

  • Lee, Ki-Don;Husain, Afzal;Kim, Kwang-Yong
    • International Journal of Fluid Machinery and Systems
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    • v.3 no.2
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    • pp.150-159
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    • 2010
  • Laidback fan shaped film-cooling hole is formulated numerically and optimized with the help of three-dimensional numerical analysis, surrogate methods, and the multi-objective evolutionary algorithm. 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 four geometric design variables, the injection angle of the hole, the lateral expansion angle of the diffuser, the forward expansion angle of the hole, and the ratio of the length to the diameter of the hole, to maximize the film-cooling effectiveness compromising with the aerodynamic loss. The objective function values are numerically evaluated through Reynolds- averaged Navier-Stokes analysis at the designs that are selected through the Latin hypercube sampling method. Using these numerical simulation results, the Response Surface Approximation model are constructed for each objective function and a hybrid multi-objective evolutionary algorithm is applied to obtain the Pareto optimal front. The clustered points from Pareto optimal front were evaluated by flow analysis. These designs give enhanced objective function values in comparison with the experimental designs.

Design Method of Multi-Stage Gear Drive (Volume Minimization and Reliability Improvement) (다단 기어장치의 설계법(체적 감소 및 신뢰성 향상))

  • Park, Jae-Hee;Lee, Joung-Sang;Chong, Tae-Hyong
    • 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.

Multi-Objective Optimization Using Kriging Model and Data Mining

  • Jeong, Shin-Kyu;Obayashi, Shigeru
    • International Journal of Aeronautical and Space Sciences
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    • v.7 no.1
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    • pp.1-12
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    • 2006
  • In this study, a surrogate model is applied to multi-objective aerodynamic optimization design. For the balanced exploration and exploitation, each objective function is converted into the Expected Improvement (EI) and this value is used as fitness value in the multi-objective optimization instead of the objective function itself. Among the non-dominated solutions about EIs, additional sample points for the update of the Kriging model are selected. The present method was applied to a transonic airfoil design. Design results showed the validity of the present method. In order to obtain the information about design space, two data mining techniques are applied to design results: Analysis of Variance (ANOVA) and the Self-Organizing Map (SOM).

Application of multi objective genetic algorithm in ship hull optimization

  • Guha, Amitava;Falzaranoa, Jeffrey
    • Ocean Systems Engineering
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    • v.5 no.2
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    • pp.91-107
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    • 2015
  • Ship hull optimization is categorized as a bound, multi variable, multi objective problem with nonlinear constraints. In such analysis, where the objective function representing the performance of the ship generally requires computationally involved hydrodynamic interaction evaluation methods, the objective functions are not smooth. Hence, the evolutionary techniques to attain the optimum hull forms is considered as the most practical strategy. In this study, a parametric ship hull form represented by B-Spline curves is optimized for multiple performance criteria using Genetic Algorithm. The methodology applied to automate the hull form generation, selection of optimization solvers and hydrodynamic parameter calculation for objective function and constraint definition are discussed here.

Prediction of Stream Flow on Probability Distributed Model using Multi-objective Function (다목적함수를 이용한 PDM 모형의 유량 분석)

  • Ahn, Sang-Eok;Lee, Hyo-Sang;Jeon, Min-Woo
    • Journal of Korean Society of Hazard Mitigation
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    • v.9 no.5
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    • pp.93-102
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    • 2009
  • A prediction of streamflow based on multi-objective function is presented to check the performance of Probability Distributed Model(PDM) in Miho stream basin, Chungcheongbuk-do, Korea. PDM is a lumped conceptual rainfall runoff model which has been widely used for flood prevention activities in UK Environmental Agency. The Monte Carlo Analysis Toolkit(MCAT) is a numerical analysis tools based on population sampling, which allows evaluation of performance, identifiability, regional sensitivity and etc. PDM is calibrated for five model parameters by using MCAT. The results show that the performance of model parameters(cmax and k(q)) indicates high identifiability and the others obtain equifinality. In addition, the multi-objective function is applied to PDM for seeking suitable model parameters. The solution of the multi-objective function consists of the Pareto solution accounting to various trade-offs between the different objective functions considering properties of hydrograph. The result indicated the performance of model and simulated hydrograph are acceptable in terms on Nash Sutcliffe Effciency*(=0.035), FSB(=0.161), and FDBH(=0.809) to calibration periods, validation periods as well.

Generalized evolutionary optimum design of fiber-reinforced tire belt structure

  • Cho, J.R.;Lee, J.H.;Kim, K.W.;Lee, S.B.
    • 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.