• Journal of Ocean Engineering and Technology
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• v.18 no.5
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• pp.29-35
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• 2004

A new approach, referred to as a successive zooming genetic algorithm (SZGA), is proposed for identifying a global solution, using continuous zooming factors for optimization problems. In order to improve the local fine-tuning of the GA, we introduced a new method whereby the search space is zoomed around the design variable with the best fitness per 100 generation, resulting in an improvement of the convergence. Furthermore, the reliability of the optimized solution is determined based on the theory of probability, and the parameter used for the successive zooming method is optimized. With parameter optimization, we can eliminate the time allocated for deciding parameters used in SZGA. To demonstrate the superiority of the proposed theory, we tested for the minimization of a multiple function, as well as simple functions. After testing, we applied the parameter optimization to a truss problem and wicket gate servomotor optimization. Then, the proposed algorithm identifies a more exact optimum value than the standard genetic algorithm.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.2
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• pp.406-414
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• 2002

A new approach, referred to as a successive zooming genetic algorithm (SZGA), is Proposed for identifying a global solution for continuous optimization problems. In order to improve the local fine-tuning capability of GA, we introduced a new method whereby the search space is zoomed around the design point with the best fitness per 100 generation. Furthermore, the reliability of the optimized solution is determined based on the theory of probability. To demonstrate the superiority of the proposed algorithm, a simple genetic algorithm, micro genetic algorithm, and the proposed algorithm were tested as regards for the minimization of a multiminima function as well as simple functions. The results confirmed that the proposed SZGA significantly improved the ability of the algorithm to identify a precise global minimum. As an example of structural optimization, the SZGA was applied to the optimal location of support points for weight minimization in the radial gate of a dam structure. The proposed algorithm identified a more exact optimum value than the standard genetic algorithms.

• Structural Engineering and Mechanics
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• v.30 no.2
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• pp.135-146
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• 2008

This study utilizes the fine-tuning and small-digit characteristics of the successive zooming genetic algorithm (SZGA) to propose a method of structural damage detection in a continuum structure, where the differences in the natural frequencies of a structure obtained by experiment and FEM are compared and minimized using an assumed location and extent of structural damage. The final methodology applied to the structural damage detection is a kind of pseudo-discrete-variable-algorithm that counts the soundness variables as one (perfectly sound) if they are above a certain standard, such as 0.99. This methodology is based on the fact that most well-designed structures exhibit failures at some critical point due to manufacturing error, while the remaining region is free of damage. Thus, damage of 1% (depending on the given standard) or less can be neglected, and the search concentrated on finding more serious failures. It is shown that the proposed method can find out the exact structural damage of the monitored structure and reduce the time and amount of computation.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.1
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• pp.36-44
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• 2005

Nowadays, various kind of rubber-like materials are used in industry. These are usually installed in automobiles, trains, etc. They work as dampers or important parts in the system, and the applications for rubber-like materials are increasing. In the past days, rubber engineers and designers predicted rubber material behaviors by the analytic method for limited problems. With the progress of digital computers, Finite Element Methods is widely used for analyzing the rubber-like materials. The popular methods predicting rubber material property are curve fitting and least square method, but there are some problems such as low precision and tedious solving process. Here, we introduce a method estimating rubber material property by successive zooming genetic algorithm(SZGA). The proposed algorithm offers more precise rubber property. To demonstrate the effectiveness of the proposed algorithm, we compared this method with Haines & Wilson's method, MARC, ABAQUS.