变步长自适应萤火虫群多模态函数优化算法.pdfVIP

Computer Engineering and Applications计算机工程与应用 2012 ,48(8) 43 变步挺自适应萤火虫群多模态函数优化算法 黄正新，周永权 HUANG Zhengxin , ZHOU Yongquan 广西民族大学数学与计算机科学学院，南宁 530006 College of Mathematics and Computer Science, Guangxi University for Nationalities , Nanning 530006, China HUANG Zhengxin , ZHOU Yongquan. Adaptive glowworm swarm optimization algorithm with changing step for optimizing multimodal functions. Computer Engineering and Applications ,2012 ,48(8) :43-47. Abstract: A new adaptive Glowworm Swarm Optimization algorithm with Changing Step(CSGSO) is presented to solve the problem that the Glowworm Swarm Optimization(GSO) algorithm to optimize 由e multi-modal function existing slow convergence and low pre- cision defects. The successful or failure to search is in仕oduced in this new algorithm. In each iteration process , the step is changed dy- namically according to the searching is successful or failure , which provides the algorithm with effective dynamic adaptability. Experi- mental results show that , the CSGSO can effectively improve the GSO algorithm to optimize the multi-modal function existing slow convergence and low precision problems. Compared with other algorithms , the CSGSO algorithm h邵 the advantages of simple opera- tion , easy to understand , fast convergence rates and high precision and so on. Key words: multimodal function optimizing; Glowworm Swarm Optimization (GSO ); adaptability; Glowworm Swarm Optimization algorithm with Changing Step (CSGSO); multimodal function 摘 要:针对萤火虫群优化(GSO) 算法优化多模态函数存在收敛速度慢和求解精度不高等缺陷，提出一种变步长白适应萤火虫 群优化算法(CSGSO) 。该算法主要思想是在GSO 算法中引入搜索成功与失败概念，在每次迭代中萤火虫个体据其投索成功或 失败，加大或减小其投索步长，使算法具有动态自适应性。实验结果表明，该算法可有效地解决GSO算法优化多模态函数存在收 敛速度慢和求解精度不高的问题，增强了 GSO算法优化多模态函数的性能;与其他算法相比，提出的算法具有操作简单、容易理 解、收敛速度快和求解精度高等优点。 关键词:多模态函数优化;萤火虫群优化(GSO); 自适应;交步长萤火虫群优化(CSGSO); 多峰函数 DOI: 1O.3778/j .issn.1002-833 1.2012.08.013 文章编号: 1002-8331 (2012)08-0043-05 文献标识码:A 中图分类号:TPI8