CAJ | 학술논문

布谷鸟搜索算法是一种基于种群迭代搜索的全局优化算法。为求解无约束优化问题，提出一种改进的布谷鸟搜索算法。利用混沌序列构造初始种群以增加群体的多样性，引入动态随机局部搜索技术对当前最优解进行局部搜索，以加快算法的收敛速度。对4个标准测试函数进行仿真实验，并与其他6种算法进行比较，结果表明，该算法具有较强的全局搜索能力和较快的收敛速度。
포곡조수색산법시일충기우충군질대수색적전국우화산법。위구해무약속우화문제，제출일충개진적포곡조수색산법。이용혼돈서렬구조초시충군이증가군체적다양성，인입동태수궤국부수색기술대당전최우해진행국부수색，이가쾌산법적수렴속도。대4개표준측시함수진행방진실험，병여기타6충산법진행비교，결과표명，해산법구유교강적전국수색능력화교쾌적수렴속도。
Cuckoo Search(CS) algorithm is proposed as a population-based optimization algorithm and it is so far successfully applied in a variety of fields. A modified CS algorithm is proposed for solving unconstrained optimization problems. Chaos sequence and dynamic random local search technique are introduced to enhance the optimization ability and to improve the convergence speed of CS algorithm. Through testing the performance of the proposed algorithm on a set of 4 benchmark functions and comparing with other six algorithms, simulation result shows that the proposed algorithm has great ability of global search and better convergence rate.