CAJ | 학술논문

对基本的布谷鸟搜索算法（Cuckoo Search，CS）进行研究，为改进CS算法局部搜索能力差、进化后期收敛速度慢、求解精度低等缺陷，考虑到二次插值法是一种局部搜索能力较强的搜索方法，提出一种基于二次插值法的布谷鸟搜索算法（QI_CS）。新算法充分利用鸟窝个体局部的优化信息，增强算法的局部搜索能力，加快算法搜索全局最优解的收敛速度。仿真实验结果表明，QI_CS 算法在保持原算法的强大全局寻优能力的基础上大幅提高算法的收敛能力和求解精度，是求解多峰函数优化问题的一种可行和有效的方法。
대기본적포곡조수색산법（Cuckoo Search，CS）진행연구，위개진CS산법국부수색능력차、진화후기수렴속도만、구해정도저등결함，고필도이차삽치법시일충국부수색능력교강적수색방법，제출일충기우이차삽치법적포곡조수색산법（QI_CS）。신산법충분이용조와개체국부적우화신식，증강산법적국부수색능력，가쾌산법수색전국최우해적수렴속도。방진실험결과표명，QI_CS 산법재보지원산법적강대전국심우능력적기출상대폭제고산법적수렴능력화구해정도，시구해다봉함수우화문제적일충가행화유효적방법。
The Cuckoo Search algorithm (CS) was studied, and in order to improve the shortcomings of the basic CS algorithm, such as low optimization precision and convergence slowly and poor local search ability in late evolution, an improved CS algorithm(QI_GSO) based on quadratic interpolation method was proposed in this paper. New algorithm makes full use of the bird’s nest local information, enhances the local search ability of the algorithm, and speeds up the convergence of the global optimal solution. The feasibility and effectiveness of the new approach was verified through testing by functions. The experimental results show that the QI_CS algorithm is significantly superior to original CS and can greatly improve the ability of seeking the global excellent result and convergence property and accuracy, which is an effective method to solve multimodal function optimization problem.