ARTICLE INFO ABSTRACT
Article history:Recently, a topology algorithm based on the artificial bee colony algorithm (ABCA) has been proposed for static and dynamic topology optimization. From the results, the convergence rate of the algorithm was determined to be slightly slow. Therefore, we propose a new search method to improve the convergence rate of the algorithm using a chaotic map. We investigate the effect of the chaotic map on the convergence rate of the algorithm in static and dynamic topology optimization. The chaotic map has been applied to three cases, namely, employ bee search, onlooker bee search, and both employ bee as well as onlooker bee search steps. It is verified that the case in which the logistic function of the chaotic map is applied to both employ bee as well as onlooker bee search steps shows the best dynamic topology optimization, improved by 5.89% compared to ABCA. Therefore, it is expected that the proposed algorithm can effectively be applied to dynamic topology optimization to improve the convergence rate. 법으로부터 자연 모방 최적화 방법인 입자 군집 최적화 (particle swarm optimization: PSO) [3] , 개미 군집 최적화(ant colony optimization: ACO) [4] 그리고 인공벌 군집 알고리즘 (artificial bee colony algorithm: ABCA) [5] 등이 있다.그 동안의 연구에서 Karaboga와 Basturk [6,7] 그리고 Omkar 등 의 카오틱 제국주의자