카오틱 맵을 이용한 위상 최적화 알고리즘의 수렴속도 향상 (Improvement of Topology Algorithm's Convergence Rate Using Chaotic Map)

서지정보

· 발행기관 : 한국생산제조학회
· 수록지 정보 : 한국생산제조학회지 / 23권 / 3호 / 279 ~ 283페이지
· 저자명 : 김용호, 김기철, 이재환, 장효재, 한석영

초록

Recently, a topology algorithm based on the artificial bee colony algorithm(ABCA) has been proposed for static and dynamic topology optimization. Fromthe results, the convergence rate of the algorithm was determined to be slightlyslow. Therefore, we propose a new search method to improve the convergencerate of the algorithm using a chaotic map. We investigate the effect of the chaoticmap on the convergence rate of the algorithm in static and dynamic topologyoptimization. The chaotic map has been applied to three cases, namely, employbee search, onlooker bee search, and both employ bee as well as onlooker beesearch steps. It is verified that the case in which the logistic function of the chaoticmap is applied to both employ bee as well as onlooker bee search steps shows thebest dynamic topology optimization, improved by 5.89% compared to ABCA.
Therefore, it is expected that the proposed algorithm can effectively be applied todynamic topology optimization to improve the convergence rate.

영어초록

Recently, a topology algorithm based on the artificial bee colony algorithm(ABCA) has been proposed for static and dynamic topology optimization. Fromthe results, the convergence rate of the algorithm was determined to be slightlyslow. Therefore, we propose a new search method to improve the convergencerate of the algorithm using a chaotic map. We investigate the effect of the chaoticmap on the convergence rate of the algorithm in static and dynamic topologyoptimization. The chaotic map has been applied to three cases, namely, employbee search, onlooker bee search, and both employ bee as well as onlooker beesearch steps. It is verified that the case in which the logistic function of the chaoticmap is applied to both employ bee as well as onlooker bee search steps shows thebest dynamic topology optimization, improved by 5.89% compared to ABCA.
Therefore, it is expected that the proposed algorithm can effectively be applied todynamic topology optimization to improve the convergence rate.

참고자료

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