• 항공우주시스템공학회지
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• 제15권2호
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• pp.1-9
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• 2021

In this paper, a thickness compensation function is introduced to consider the shear deformation and warping effect resulting from increased thickness in the composite multi-cell wing box. The thickness compensation function is used to perform the structure optimization of the multi-cell. It is determined by minimizing the error of an analytical formula using solid mechanics and the Ritz method. It is used to define a structural performance prediction expression due to the increase in thickness. The parameter is defined by the number of spars and analyzed by the critical buckling load and the limited failure index as a response. Constraints in structural optimization are composed of displacements, torsional angles, the critical buckling load, and the failure index. The objective function is the mass, and its optimization is performed using a genetic algorithm.

• Journal of applied mathematics & informatics
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• 제24권1_2호
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• pp.449-460
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• 2007

Several fuzzy approaches can be considered for solving multi-objective transportation problem. This paper presents a fuzzy goal programming approach to determine an optimal compromise solution for the multiobjective transportation problem. We assume that each objective function has a fuzzy goal. Also we assign a special type of nonlinear (hyperbolic) membership function to each objective function to describe each fuzzy goal. The approach focuses on minimizing the negative deviation variables from 1 to obtain a compromise solution of the multiobjective transportation problem. We show that the proposed method and the fuzzy programming method are equivalent. In addition, the proposed approach can be applied to solve other multiobjective mathematical programming problems. A numerical example is given to illustrate the efficiency of the proposed approach.

• Structural Engineering and Mechanics
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• 제84권4호
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• pp.489-502
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• 2022

This paper is aimed at developing an optimization-based Finite Element model updating approach for structural damage identification and quantification. A modal flexibility-based error function is introduced, which uses modal assurance criterion to formulate the updating problem as an optimization problem. Because of the inexplicit input/output relationship between the candidate solutions and the error function's output, a robust and efficient optimization algorithm should be employed to evaluate the solution domain and find the global extremum with high speed and accuracy. This paper proposes a new multi-stage Selective Particle Swarm Optimization (SPSO) algorithm to solve the optimization problem. The proposed multi-stage strategy not only fixes the premature convergence of the original Particle Swarm Optimization (PSO) algorithm, but also increases the speed of the search stage and reduces the corresponding computational costs, without changing or adding extra terms to the algorithm's formulation. Solving the introduced objective function with the proposed multi-stage SPSO leads to a smart feedback-wise and self-adjusting damage detection method, which can effectively assess the health of the structural systems. The performance and precision of the proposed method are verified and benchmarked against the original PSO and some of its most popular variants, including SPSO, DPSO, APSO, and MSPSO. For this purpose, two numerical examples of complex civil engineering structures under different damage patterns are studied. Comparative studies are also carried out to evaluate the performance of the proposed method in the presence of measurement errors. Moreover, the robustness and accuracy of the method are validated by assessing the health of a six-story shear-type building structure tested on a shake table. The obtained results introduced the proposed method as an effective and robust damage detection method even if the first few vibration modes are utilized to form the objective function.

• 대한전기학회논문지
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• 제41권9호
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• pp.984-993
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• 1992

Fuzzy L.P. with Multi-criteria function is adopted in this VAR planning algorithm to accomplish the optimization of comflicting objectives, such as the amount of the VAR installed and power system loss, while keeping the bus voltage profile within an admissible range. Fuzzy L.P. with Multi-criteria function, a powerful tool dealing with the fuzziness of satisfaction levels of the constraints and the goal of objective functions, enables us to search for the solutions which may contribute in VAR planning. This advantage is not provided by traditional standardized L.P. The effectiveness of the proposed algorithm has been verified by the test on the IEEE-30 bus system.

• International Journal of Control, Automation, and Systems
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• 제6권3호
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• pp.401-412
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• 2008

This paper discusses a design methodology of cooperative path planning for dynamical multi-agent systems with spatial and temporal constraints. The cooperative behavior of the multi-agent systems is specified in terms of the objective function in an optimization formulation. The path of achieving cooperative tasks is then generated by the optimization formulation constructed based on a differential flatness approach. Three scenarios of multi-agent tasking are proposed at the cooperative task planning framework. Given agent dynamics, both spatial and temporal constraints are considered in the path planning. The path planning algorithm first finds trajectory curves in a lower-dimensional space and then parameterizes the curves by a set of B-spline representations. The coefficients of the B-spline curves are further solved by a sequential quadratic programming solver to achieve the optimization objective and satisfy these constraints. Finally, several illustrative examples of cooperative path/task planning are presented.

• 제어로봇시스템학회논문지
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• 제15권1호
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• pp.67-71
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• 2009

This paper presents a rank-constrained linear matrix inequality (LMI) approach to the design of a multi-objective controller such as $H_2/H_{\infty}$ control. Multi-objective control is formulated as an LMI optimization problem with a nonconvex rank condition, which is imposed on the controller gain matirx not Lyapunov matrices. With this rank-constrained formulation, we can expect to reduce conservatism because we can use separate Lyapunov matrices for different control objectives. An iterative penalty method is applied to solve this rank-constrained LMI optimization problem. Numerical experiments are performed to illustrate the proposed method.

• 소성∙가공
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• 제30권1호
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• pp.27-34
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• 2021

In this study, response surface method (RSM) was used in modeling and multi-objective optimization of the parameters of AA5052-H32 in incremental sheet forming (ISF). The goals of optimization were the maximum forming angle, minimum thickness reduction, and minimum surface roughness, with varying values in response to changes in 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 for modeling the variations in the forming angle, thickness reduction, and surface roughness in response to variations in process parameters. Subsequently, the RSM model was used as the fitness function for multi-objective optimization of the ISF process based on experimental design. The results showed that RSM can be effectively used to control the forming angle, thickness reduction, and surface roughness.

• Journal of Electrical Engineering and Technology
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• 제6권6호
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• pp.853-866
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• 2011

We propose a multi-objective space search algorithm (MSSA) and introduce the identification of fuzzy inference systems based on the MSSA and information granulation (IG). The MSSA is a multi-objective optimization algorithm whose search method is associated with the analysis of the solution space. The multi-objective mechanism of MSSA is realized using a non-dominated sorting-based multi-objective strategy. In the identification of the fuzzy inference system, the MSSA is exploited to carry out parametric optimization of the fuzzy model and to achieve its structural optimization. The granulation of information is attained using the C-Means clustering algorithm. The overall optimization of fuzzy inference systems comes in the form of two identification mechanisms: structure identification (such as the number of input variables to be used, a specific subset of input variables, the number of membership functions, and the polynomial type) and parameter identification (viz. the apexes of membership function). The structure identification is developed by the MSSA and C-Means, whereas the parameter identification is realized via the MSSA and least squares method. The evaluation of the performance of the proposed model was conducted using three representative numerical examples such as gas furnace, NOx emission process data, and Mackey-Glass time series. The proposed model was also compared with the quality of some "conventional" fuzzy models encountered in the literature.

• 한국강구조학회 논문집
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• 제12권5호통권48호
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• pp.503-513
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• 2000

본 연구에서는 구조물의 최적설계문제를 다를 때 나타나는 퍼지성을 고려하는 동시에 대립되는 기준들을 다루기 위해 중요도를 적용 유전자알고리즘 및 퍼지이론에 의한 이산형의 다목적 함수를 갖는 트러스구조물의 최적화를 시도하는 다목적 이산화 최적화 프로그램을 개발하였다. 그리고 개발된 프로그램을 적용하여 10부재철골트러스에 대한 설계 예를 들어 비교 고찰하였다. 본 연구를 통해 평면트러스구조물에대한 응력해석 및 최적설계가 일률적으로 처리될 수 있는 통합 시스템화된 퍼지-유전자알고리즘에 의한 다목적최적 구조설계가 가능하게 되었다. 특히 일반최적설계에서 처리되지 않는 불확실한 제약조건에 대한 경우에 대하여도 피지이론을 도입함으로써 가능하게 되어 보다 구조물의 합리적인 최적설계가 가능하게 되었다.

• 국제학술발표논문집
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• The 2th International Conference on Construction Engineering and Project Management
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• pp.509-519
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• 2007

Private financing is playing an increasing role in public infrastructure construction projects worldwide. However, private investors/operators are exposed to the financial risk of low profitability due to the inaccurate estimation of facility demand, operation income, maintenance costs, etc. From the operator's perspective, a sound and thorough financial feasibility study is required to establish the appropriate capital structure of a project. Operators tend to reduce the equity amount to minimize the level of risk exposure, while creditors persist to raise it, in an attempt to secure a sufficient level of financial involvement from the operators. Therefore, it is important for creditors and operators to reach an agreement for a balanced capital structure that synthetically considers both profitability and repayment capacity. This paper presents an optimal capital structure model for successful private infrastructure investment. This model finds the optimized point where the profitability is balanced with the repayment capacity, with the use of the concept of utility function and multi-objective GA (Generic Algorithm)-based optimization. A case study is presented to show the validity of the model and its verification. The research conclusions provide a proper capital structure for privately-financed infrastructure projects through a proposed multi-objective model.