• Steel and Composite Structures
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• v.19 no.2
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• pp.349-368
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• 2015

This paper presents a new algorithm to find the optimal distribution of steel diagonal braces (SDB) using artificial bee colony optimization technique. The four different objective functions are employed based on the transfer function amplitude of; the top displacement, the top absolute acceleration, the base shear and the base moment. The stiffness parameter of SDB at each floor level is taken into account as design variables and the sum of the stiffness parameter of the SDB is accepted as an active constraint. An optimization algorithm based on the Artificial Bee Colony (ABC) algorithm is proposed to minimize the objective functions. The proposed ABC algorithm is applied to determine the optimal SDB distribution for planar buildings in order to rehabilitate existing planar steel buildings or to design new steel buildings. Three planar building models are chosen as numerical examples to demonstrate the validity of the proposed method. The optimal SDB designs are compared with a uniform SDB design that uniformly distributes the total stiffness across the structure. The results of the analysis clearly show that each optimal SDB placement, which is determined based on different performance objectives, performs well for its own design aim.

• Journal of Korean Society of Steel Construction
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• v.16 no.2 s.69
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• pp.201-213
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• 2004

A GA-based optimum design algorithm and a program for plane steel frame structures with semi-rigid connections are presented. The algorithm is incorporated with the refined plastic hinge analysis method wherein geometric nonlinearity is considered by using the stability functions of beam-column members, and material nonlinearity, by using the gradual stiffness degradation model that includes the effects of residual stresses, moment redistribution through the occurrence of plastic hinges, semi-rigid connections, and geometric imperfection of members. In the genetic algorithm, the tournament selection method and micro-GAs are employed. The fitness function for the genetic algorithm is expressed as an unconstrained function composed of objective and penalty functions. The objective and penalty functions are expressed as the weight of steel frames and the constraint functions, respectively. In particular, the constraint functions fulfill the requirements of load-carrying capacity, serviceability, ductility, and construction workability. To verify the appropriateness of the present method, the optimal design results of two plane steel frames with rigid and semi-rigid connections are compared.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.7 no.3
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• pp.3-11
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• 2008

Robust design pioneered by Dr. G. Taguchi has been applied to versatile engineering problems for improving quality. Since 1980s, the Taguchi method has been introduced to numerical optimization, complementing the deficiencies of deterministic optimization, which is often called the robust optimization. In this study, the robust optimization strategy is proposed by considering the robustness of objective and constraint functions. The statistics of responses in the functions are surrogated by kriging models. In addition, objective and/or constraint function is represented by the probability of success, thus facilitating robust optimization. The mathematical problem and the two-bar design problem are investigated to show the validity of the proposed method.

• Journal of the Korean Mathematical Society
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• v.41 no.5
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• pp.821-864
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• 2004

Both parametric and parameter-free necessary and sufficient optimality conditions are established for a class of nondiffer-entiable nonconvex optimal control problems with generalized fractional objective functions, linear dynamics, and nonlinear inequality constraints on both the state and control variables. Based on these optimality results, ten Wolfe-type parametric and parameter-free duality models are formulated and weak, strong, and strict converse duality theorems are proved. These duality results contain, as special cases, similar results for minmax fractional optimal control problems involving square roots of positive semi definite quadratic forms, and for optimal control problems with fractional, discrete max, and conventional objective functions, which are particular cases of the main problem considered in this paper. The duality models presented here contain various extensions of a number of existing duality formulations for convex control problems, and subsume continuous-time generalizations of a great variety of similar dual problems investigated previously in the area of finite-dimensional nonlinear programming.

• IE interfaces
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• v.15 no.1
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• pp.49-54
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• 2002

A bus scheduling problem with multiple objective functions and travel time constraint is to determine the allocation of buses to customer service requests minimizing the number of buses and travel costs under the travel time restriction for each bus. For the scheduling, we first represent the scheduling problem using a graph and develop a hierarchical approach. Second, we develop a mathematical model based algorithm for the scheduling problem including heuristic methods. We tested the performance of the algorithm on instances with real data. As a result, the total number of buses and travel costs are reduced over about 10% comparing with that of current practice at the company.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.12 no.20
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• pp.97-104
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• 1989

In this thesis, each objective functions with hierarchical system Bi-level linear programming (BLPP) Problem applications to fuzzy set theory conducted multiple objective programming problem. Using linear fuzzy membership functions make a change typical BLPP and presents modified method turn to account established BLPP method, presents operation results lead to example. Fuzzy Bi-level linear programming problem (FBLPP) can be natural describe realities of life then BLPP.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.61
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• pp.41-50
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• 2000

Cell formation(CF) Is to group parts with similar geometry, function, material and process into part families, and the corresponding machines into machine cells. Cell formation solutions often contain exceptional elements(EEs). Also, the following objective functions - minimizing the total costs of dealing with exceptional elements and maximizing total similarity coefficients between parts - have been used in CF modeling. Thus, multiobjective programming approach can be developed to model cell formation problems with two conflicting objective functions. This paper presents an effective cell formation method with fuzzy multiobjective nonlinear mixed-integer programming simultaneously to form machine cells and to minimize the cost of eliminating EEs.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.5
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• pp.319-322
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• 2016

We propose a parametric blind deconvolution method for bi-level images with unknown intensity levels that estimates unknown parameters for point spread functions and images by minimizing a penalized nonlinear least squares objective function based on normalized correlation coefficients and two regularization functions. Unlike conventional methods, the proposed method does not require knowledge about true intensity values. Moreover, the objective function of the proposed method can be effectively minimized, since it has the special structure of nonlinear least squares. We demonstrate the effectiveness of the proposed method through simulations and experiments.

• Journal of applied mathematics & informatics
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• v.6 no.1
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• pp.79-88
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• 1999

This work aims to establish an algorithm for solving the problem of convex programming with several objective-functions with linear constraints. Starting from the idea of Rosen's algorithm for solving the problem of convex programming with linear con-straints and taking into account the solution concept from multi-dimensional programming represented by a program which reaches "the best compromise" we are extending this method in the case of multidimensional programming. The concept of direction of min-imization is introduced and a necessary and sufficient condition is given for a s∈Rn direction to be a direction is min-imal. The two numerical examples presented at the end validate the algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.42-45
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• 2002

In this paper, multiphase optimization of machine Tool structure is presented. The final goal is to obtain 1) light weight, 2) statically and dynamically rigid. and 3) thermally stable structure. The entire optimization process is carried out in three phases. In the first phase, multiple static optimization problem with two objective functions is treated using Pareto genetic algorithm. where two objective functions are weight of the structure and static compliance. In the second phase, maximum receptance is minimized using simple genetic algorithm. And the last phase, thermal deflection to moving heat sources is analyzed using Predictor-Corrector Method. The method is applied to a high speed line center design which takes the shape of back-column structure.