• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.1 s.173
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• pp.215-224
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• 2000

In order to reduce the variation effects of uncertainties in the engineering environments, new robust optimization method, which considers the uncertainties in design process, is proposed. Both design variables and system parameters are considered as random variables about their nominal values. To ensure the robustness of performance function, a new objective is set to minimize the variance of that function. Constraint variations are handled by introducing probability constraints. Probability constraints are solved by the advanced first order second moment (AFOSM) method based on the reliability theory. The proposed robust optimization method has an advantage that the second derivatives of the constraints are not required. The suggested method is examined by solving three examples and the results are compared with those for deterministic case and those available in literature.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.6
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• pp.106-118
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• 1998

The study concerns a design procedure of rule-based systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient from of "IF..., THEN..." statements, and exploits the theory of system optimization and fuzzy implication rules. The method for rule-based fuzzy modeling concerns the from of the conclusion part of the the rules that can be constant. Both triangular and Gaussian-like membership function are studied. The optimization hinges on an autotuning algorithm that covers as a modified constrained optimization method known as a complex method. The study introduces a weighted performance index (objective function) that helps achieve a sound balance between the quality of results produced for the training and testing set. This methodology sheds light on the role and impact of different parameters of the model on its performance. The study is illustrated with the aid of two representative numerical examples.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.2
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• pp.114-126
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• 2009

In this paper, a versatile multi-criteria optimization concept for fatigue life prediction is introduced. Multi-criteria decision making in engineering design refers to obtaining a preferred optimal solution in the context of conflicting design objectives. Compromise decision support problems are used to model engineering decisions involving multiple trade-offs. These methods typically rely on a summation of weighted attributes to accomplish trade-offs among competing objectives. This paper gives an interpretation of the decision parameters as governing both the relative importance of the attributes and the degree of compensation between them. The approach utilizes a response surface model, the compromise decision support problem, which is a multi-objective formulation based on goal programming. Examples illustrate the concepts and demonstrate their applicability.

• International Journal of Korean Welding Society
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• v.3 no.1
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• pp.2-7
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• 2003

Laser welding application for car body manufacturing has many advantages in the stiffness and the lightness of vehicle, the productivity of assembly line, and the degree of freedom in design. This presentation will express the innovation of car body manufacturing including parameter optimization, process modeling, and system integration. In this application the investment for systems was cut down dramatically by real time switching over the laser path between two welding stations. Points of technical discussion are as follows; optimization of parameters such as laser power, robot speed and trajectory, compact and useful design of jig & fixture to assure welding quality for 3 sheet-layer zinc-coated steel, system integration between 4㎾ Nd:YAG laser device and the other systems, on-line real time welding quality monitoring system, perfect safety standards for high power laser, minimization of consumption costs such as arc lamp, protective glass for optic, etc. This application was successfully launched mass production line in 2001. The laser-welded line of side panel consists of 122 stitches totally. And the length is about 2.4m.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.826-827
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• 2011

The selection of optimum parameters in electromagnetic design usually requires optimization of multimodal, non linear functions. This leads to extensive calculations which pose a huge inconvenience in the design process. This paper proposes a novel algorithm for dealing efficiently with this issue. Through the use of contour line concept coupled with Kriging, the algorithm finds out all the peaks in the problem domain with as few function calls as possible. The proposed algorithm is applied to the magnet shape optimization of an axial flux permanent magnet synchronous machine and the cogging torque was reduced to 79.8% of the initial one.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.753-755
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• 2005

The Black-Scholes (BS) option pricing model is a landmark in contingent claim theory and has found wide acceptance in financial markets. However, it has a difficulty in the use of the model, because the volatility which is a nonlinear function of the other parameters must be estimated. The more accurately investors are able to estimate this value, the more accurate their estimates of theoretical option values will be. This paper proposes a new model which is based on Particle Swarm Optimization (PSO) for finding more precise theoretical values of options in the field of evolutionary computation (EC) than genetic algorithm (GA)or calculus-based search techniques to find estimates of the implied volatility.

• Journal of Ocean Engineering and Technology
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• v.21 no.2 s.75
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• pp.75-80
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• 2007

The design variables for structural systems, in most practical designs, are chosen from a list of discrete values, which are commercially available sizing. This paper presents the application of Genetic Algorithms for determining the optimum design for two-dimensional structures with discrete and pseudocontinuous design variables. Genetic Algorithms are heuristic search algorithms and are effective tools for finding global solutions for discrete optimization. In this paper, Genetic Algorithms are used as the method of Elitism and penalty parameters, in order to improve fitness in the reproduction process. Examples in this paper include: 10 bar planar truss and 1 bay 8-story frame. Truss with discrete and pseudoucontinuous design variables and steel frame with W-sections are used for the design of discrete optimization.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.2
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• pp.168-175
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• 2003

A new algorithm based on Genetic Algorithms is proposed f3r solving process optimization problems formulated in MINLP, GDP and hybrid MINLP/GDP. This work is focused especially on the design of the Genetic Algorithm suitable to handle disjunctive programming with the same level of MINLP handling capability. Hybridization with the Simulated Annealing is experimented and many heuristics are adopted. Real and binary coded Genetic Algorithm initiates the global search in the entire search space and at every stage Simulated Annealing makes the candidates to climb up the local hills. Multi-Niche Crowding method is adopted as the multimodal function optimization technique. and the adaptation of probabilistic parameters and dynamic penalty systems are also implemented. New strategies to take the logical variables and constraints into consideration are proposed, as well. Various test problems selected from many fields of process systems engineering are tried and satisfactory results are obtained.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.4
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• pp.735-741
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• 2009

This paper presents an algorithm for the permanent magnet shape optimization of a large scale BLDC(Brushless DC) motor to minimize the cogging torque. A response surface method (RSM) using multiquadric radial basis function is employed to interpolate the objective function in design parameter space. In order to get a reasonable response surface with relatively small number of sampling data points, additional sampling points are added on the basis of design sensitivity analysis computed by using FEM. The algorithm has 2 stages: the first stage is to determine the PM arc angle, and the 2nd stage is to optimize the magnet pole shape. The developed algorithm is applied to a 5MW BLDC motor to get a minimum cogging torque. After 3 iterations with 4 design parameters, the cogging torque is reduced to 13.2% of the initial one.

• Industrial Engineering and Management Systems
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• v.13 no.1
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• pp.43-51
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• 2014

This study presents an application of adaptive particle swarm optimization (APSO) to solving the bi-level job-shop scheduling problem (JSP). The test problem presented here is $10{\times}10$ JSP (ten jobs and ten machines) with tribottleneck machines formulated as a bi-level formulation. APSO is used to solve the test problem and the result is compared with the result solved by basic PSO. The results of the test problem show that the results from APSO are significantly different when compared with the result from basic PSO in terms of the upper level objective value and the iteration number in which the best solution is first identified, but there is no significant difference in the lower objective value. These results confirmed that the quality of solutions from APSO is better than the basic PSO. Moreover, APSO can be used directly on a new problem instance without the exercise to select parameters.