计算机工程与应用2018，Vol.54Issue(12)：1-9,9.DOI:10.3778/j.issn.1002-8331.1803-0260
新型群智能优化算法综述
Summary of new group intelligent optimization algorithms
摘要
Abstract
Traditional swarm intelligent algorithms have some shortcomings in solving complex practical multi-objective optimization problems. In recent years, scholars have proposed many new swarm intelligent algorithms with strong appli-cability and have achieved good experimental results in solving complex practical problems. In this paper, it summarizes new swarm intelligent algorithms including Bacterial Foraging Optimization(BFO), Shuffled Frog Leaping Algorithm (SFLA), Artificial Bee Colony(ABC), Glowworm Swarm Optimization(GSO), Cuckoo Search(CS), Fruit Fly Optimi-zation Algorithm(FOA)and Brain Storm Optimization(BSO). Finally, further research direction about it will be discussed.关键词
细菌觅食优化/混合蛙跳算法/人工蜂群算法/萤火虫算法/布谷鸟搜索/果蝇优化算法/头脑风暴优化算法Key words
bacterial foraging optimization/shuffled frog leaping algorithm/artificial bee colony/glowworm swarm opti-mization/cuckoo search/fruit fly optimization algorithm/brain storm optimization分类
信息技术与安全科学引用本文复制引用
林诗洁,董晨,陈明志,张凡,陈景辉..新型群智能优化算法综述[J].计算机工程与应用,2018,54(12):1-9,9.基金项目
国家自然科学基金(No.61672159) （No.61672159）
福建省科技厅区域发展项目(No.2015H4005) （No.2015H4005）
福建省科技厅工业引导性(重点)项目(No.2015H0020) （重点）
福建省教育厅项目(No.JAT170099) （No.JAT170099）
校科技发展基金(No.2014-XY-19). （No.2014-XY-19）
