计算机工程Issue(5)：168-172,5.DOI:10.3969/j.issn.1000-3428.2014.05.035
基于模糊阈值补偿的混合蛙跳算法
Shuffled Frog Leaping Algorithm Based on Fuzzy Threshold Compensation
摘要
Abstract
To solve the problem of slow convergence speed and low optimization precision of Shuffled Frog Leaping Algorithm (SFLA) in solving complex problems, a Shuffled Frog Leaping Algorithm Based on Fuzzy Threshold Compensation(FTCSFLA) is proposed. The fuzzy grouping idea is introduced to divide different frogs into fuzzy groups, and disturbance strategy in a local search is improved based on the basic SFLA. Each fuzzy group is defined with a total membership threshold and a total compensation coefficient, and each frog is defined with a fuzzy membership, which is scaled with the distribution degree of neighborhood frogs. In a local search, the worst individual is updated by two methods in each group, which is partitioned according to the relation between fuzzy membership and membership threshold. In two methods, a compensation coefficient is set to give a unify expression. Experimental results show that the convergence precision and speed of FTCSFLA which membership threshold is 0.9 is better than SFLA and FTCSFLA which membership threshold is 0.5. The evolution curve shows that the convergence precision and speed of FTCSFLA is the optimum when its membership threshold is between (0.5, 0.9].关键词
混合蛙跳算法/模糊隶属度/隶属度阈值/补偿系数/模糊分组/扰动策略/优化性能Key words
Shuffled Frog Leaping Algorithm(SFLA)/fuzzy membership/membership threshold/compensation coefficient/fuzzy grouping/disturbance strategy/optimization performance分类
信息技术与安全科学引用本文复制引用
刘立群,王联国,火久元,韩俊英,刘成忠..基于模糊阈值补偿的混合蛙跳算法[J].计算机工程,2014,(5):168-172,5.基金项目
国家自然科学基金资助项目(61063028)；中国博士后科学基金资助项目(2013M542398)；甘肃省高等学校研究生导师科研基金资助项目(1202-04,1102-05)；甘肃省教育厅信息化战略研究基金资助项目(2011-02)；甘肃省自然科学研究计划基金资助项目(1308RJZA214,1208RJZA133)；甘肃农业大学盛彤笙科技创新基金资助项目(GSAU-STS-1322)；兰州交通大学青年科学基金资助项目(2013032)。 （61063028）
