计算机工程与应用2016，Vol.52Issue(19)：161-166,6.DOI:10.3778/j.issn.1002-8331.1604-0110
自适应精英反向学习共生生物搜索算法
Symbiotic organisms search algorithm using adaptive elite opposition-based learning
摘要
Abstract
Aiming at the problems of poor convergence, low searching precision and ease of premature convergence when solving the complex optimization problems, combining with adaptive strategy, an improved SOS algorithm with dif-ferent difference perturbation terms and elite opposition-base learning strategy is proposed. Experiments are conducted on the 14 benchmark functions and the results show that the improved SOS algorithm has obviously better performance in convergence speed, solution precision and global optimization than SOS algorithm and other three algorithms.关键词
共生生物搜索算法/差分扰动/自适应/精英反向学习Key words
Symbiotic Organisms Search(SOS)/difference perturbation/adaptive adjustment/elite opposition-based learning分类
信息技术与安全科学引用本文复制引用
周虎,赵辉,周欢,王骁飞..自适应精英反向学习共生生物搜索算法[J].计算机工程与应用,2016,52(19):161-166,6.基金项目
国家自然科学基金（No.71501184）。 （）
