计算机工程与应用2016，Vol.52Issue(16)：23-29,45,8.DOI:10.3778/j.issn.1002-8331.1511-0205
一种强化互学习的人工蜂群算法
Enhanced mutual learning artificial bee colony algorithm
摘要
Abstract
In order to deal with the basic ABC algorithm for its slow convergence, tending to get stagnation on local optima, and further to improve its searching efficiency in exploration and exploitation, this paper proposes an improved artificial bee colony algorithm called Enhanced Mutual Learning ABC algorithm(EMLABC), applying different kind of honey bees with distinguished strategies, firstly for employed bees, by exemplifying mutation perturbation learning frequency and basing on multi comparatively prior neighbors for learning, to enhance global exploration and avoid premature, and then applying onlooker bees with extensive mutual learning strategy, which can enable the new candidate solutions more likely to search in potential better space, thus to achieve fast convergence and accuracy. The experiments are conducted on a benchmark suite of 16 unimodal and multimodal test functions, the results demonstrate significant improvements of EMLABC when compared with the basic ABC algorithm and several recent variants of ABC algorithm.关键词
人工蜂群算法/群体智能/数值函数优化/互学习Key words
artificial bee colony/swarm intelligence/numerical function optimization/mutual learning分类
信息技术与安全科学引用本文复制引用
罗浩,刘宇..一种强化互学习的人工蜂群算法[J].计算机工程与应用,2016,52(16):23-29,45,8.基金项目
国家自然科学基金委员会与中国民用航空局联合资助项目（No.U1233110）；中央高校基本科研业务费（No.DUT13JR01）。 （）
