• Proceedings of the KSME Conference
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• 2001.06c
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• pp.559-564
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• 2001

The design of an automobile mirror actuator system needs a systematic optimization due to several variables, constraints, geometric limitations, moving angle, and so on. Therefore, this article provides the procedure of a genetic algorithm(GA) based optimization with finite element analysis for design of a mirror actuator considering design constraints, geometric limitations, moving angle. Local optimum problem in optimization design with sensitivity analysis is overcome by using zero-order overall searching method which is new optimization design method using a genetic algorithm.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.1
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• pp.158-166
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• 2003

The paper describes the development and application of advanced evolutionary computing techniques referred to as micro genetic algorithms ($\mu$GA) in the context of engineering design optimization. The basic concept behind $\mu$GA draws from the use of small size of population irrespective of the bit string length in the representation of design variable. Such strategies also demonstrate the faster convergence capability and more savings in computational resource requirements than simple genetic algorithms (SGA). The paper first explores ten-bar truss design problems to see the optimization performance between $\mu$GA and SGA. Subsequently, $\mu$GA is applied to a realistic engineering design problem in the injection molding process optimization.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.231-236
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• 2001

The goal of multiphase optimization of machine structure is to obtain 1) light weight, 2) statically and dynamically rigid structure. The entire optimization process is carried out in two phases. In the first phase, multiple optimization problem with two objective functions is treated using pareto genetic algorithm. Two objective functions are weight of the structure, and static compliance. In the second phase, maximum receptance is minimized using genetic algorithm. The method is applied to design of quill type machine structure with back column.

• Journal of Ocean Engineering and Technology
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• v.24 no.6
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• pp.109-113
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• 2010

We present the minimum weight optimum design of cross sectional for frame structures subject to natural frequency. The optimum design in this paper employ discrete and continuous design variables and Genetic Algorithms. In this paper, Genetic Algorithms is used in optimization process, and be used the method of Elitism and penalty parameters in order to improved fitness in the reproduction process. For 1-Bay 2-Story frame structure, in examples, continuous and discrete design variables are used, and W-section (No.1~No.64), from AISC, discrete data are used in discrete optimization. In this case, Exhaustive search are used for finding global optimum. Continuous variables are used for 1-Bay 7-Story frame structure. Two typical frame structure optimization examples are employed to demonstrate the availability of Genetic Algorithms for solving minimum weight optimum of frame structures with fundamental and multi frequency.

• International Journal of Aeronautical and Space Sciences
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• v.14 no.4
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• pp.341-349
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• 2013

This paper considers the optimum design of flexbeam cross-sections for a full-scale bearingless helicopter rotor, using an efficient hybrid optimization algorithm based on particle swarm optimization, and an improved genetic algorithm, with an effective constraint handling scheme for constrained nonlinear optimization. The basic operators of the genetic algorithm, of crossover and mutation, are revisited, and a new rank-based multi-parent crossover operator is utilized. The rank-based crossover operator simultaneously enhances both the local, and the global exploration. The benchmark results demonstrate remarkable improvements, in terms of efficiency and robustness, as compared to other state-of-the-art algorithms. The developed algorithm is adopted for two baseline flexbeam section designs, and optimum cross-section configurations are obtained with less function evaluations, and less computation time.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2069-2074
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• 2016

This paper presents a methodology based on evolutionary optimization for simultaneously optimizing design parameters of controller and components of plant. Genetic programming(GP) based bond graph model generation is adopted to open-ended search for the plant. Also GP is applied to represent the controller with a unified method. The formulations of simultaneous plant-controller design optimization problem and the description of solution techniques based on bond graph are derived. A feasible solutions for a plant/controller design using the simultaneous optimization methodology is illustrated.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.1
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• pp.90-99
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• 2003

Object clustering, which makes classification for a set of objects into a number of groups such that objects included in a group have similar characteristic and objects in different groups have dissimilar characteristic each other, has been exploited in diverse area such as information retrieval, data mining, group technology, etc. In this study, an object-clustering problem with similarity coefficients between objects is considered. At first, an evaluation function for the optimization problem is defined. Then, a genetic algorithm and local optimization technique based on heuristic method are proposed and used in order to obtain near optimal solutions. Solutions from the genetic algorithm are improved by local optimization techniques based on object relocation and cluster merging. Throughout extensive experiments, the validity and effectiveness of the proposed algorithms are tested.

• International Journal of Automotive Technology
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• v.8 no.5
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• pp.563-573
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• 2007

Optimal design of an automotive catalytic converter for minimization of cold-start emissions is numerically performed using a micro genetic algorithm for two optimization problems: optimal geometry design of the monolith for various operating conditions and optimal axial catalyst distribution. The optimal design process considered in this study consists of three modules: analysis, optimization, and control. The analysis module is used to evaluate the objective functions with a one-dimensional single channel model and the Romberg integration method. It obtains new design variables from the control module, produces the CO cumulative emissions and the integral value of a catalyst distribution function over the monolith volume, and provides objective function values to the control module. The optimal design variables for minimizing the objective functions are determined by the optimization module using a micro genetic algorithm. The control module manages the optimal design process that mainly takes place in both the analysis and optimization modules.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.696-702
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• 2006

Flutter characteristics of composite curved wing were investigated in this study. The efficient and robust system for the flutter optimization of general composite curved wing models has been developed using the coupled computational method based on both the standard genetic algorithm and the micro genetic algorithms. Micro genetic algorithm is used as an alternative method to overcome the relatively poor exploitation characteristics of the standard genetic algorithm. The present results show that the micro genetic algorithm is more efficient in order to find optimized lay-ups for a composite curved wing model. It is found that the flutter stability of curved wing model can be significantly increased using composite materials with proper optimum lamination design when compared to the case of isotropic wing model under the same weight condition.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.1
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• pp.86-93
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• 2007

A new operation termed competitive generation in the processes of genetic algorithms is proposed for accelerating the optimization speed of genetic algorithms. The competitive generation devised by considering the competition of sperms for fertilization provides a good opportunity for the genetic algorithms to approach global optimum without falling into local optimum. Experimental results with typical problems showed that the genetic algorithms with competitive generation are superior to those without the competitive generation.