计算机工程与应用Issue(7)：50-55,6.DOI:10.3778/j.issn.1002-8331.1301-0274
自适应调整参数的果蝇优化算法
Fruit fly optimization algorithm with adaptive parameter
摘要
Abstract
In order to overcome the problems of FOA, such as low convergence precision and unstable convergence resulted from improper random parameter, an improved FOA is proposed, called Fruit Fly Optimization Algorithm with Adaptive Parameter(FOAAP). In each evolutionary generation, the accurate values describing the characteristics of the overall species are input, 3 digital characteristics C(Ext'Ent'Het) of the contemporary cloud model are obtained by backward cloud generator, then using U conditions membership cloud generator, the parameter Value is adaptively adjusted, which is Fruit Fly’s searching distance and direction for food. FOAAP is compared with FOA and other algorithms in reference literatures, experimental results show that FOAAP has the advantages of speeder convergence, higher convergence preci-sion and higher convergence reliability.关键词
云模型/自适应/果蝇优化算法/收敛精度Key words
cloud model/adaptive/Fruit Fly Optimization Algorithm(FOA)/convergence precision分类
信息技术与安全科学引用本文复制引用
韩俊英,刘成忠..自适应调整参数的果蝇优化算法[J].计算机工程与应用,2014,(7):50-55,6.基金项目
甘肃省自然科学基金（No.1208RJZA133）；甘肃省科技支撑计划（No.1011NKCA058）；甘肃省教育厅科研基金（No.1202-04）。 （）
