• Title, Summary, Keyword: 주밍 인자

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Convergence Enhanced Successive Zooming Genetic Algorithm far Continuous Optimization Problems (연속 최적화 문제에 대한 수렴성이 개선된 순차적 주밍 유전자 알고리듬)

  • Gwon, Yeong-Du;Gwon, Sun-Beom;Gu, Nam-Seo;Jin, Seung-Bo
    • 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.

Optimization and Verification of Parameters Used in Successive Zooming Genetic Algorithm (순차적 주밍 유전자 알고리즘 기법에 사용되는 파라미터의 최적화 및 검증)

  • KWON YOUNG-DOO;KWON HYUN-WOOK;KIM JAE-YONG;JIN SEUNG-BO
    • 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.