### Enhanced mutual learning artificial bee colony algorithm

LUO Hao1，2, LIU Yu1，2

1. 1.School of Software, Dalian University of Technology, Dalian, Liaoning 116024, China
2.Institute of IT Service Engineering and Management, Dalian University of Technology, Dalian, Liaoning 116024, China
• Online:2016-08-15 Published:2016-08-12

### 一种强化互学习的人工蜂群算法

1. 1.大连理工大学 软件学院，辽宁 大连 116024
2.大连理工大学 IT服务工程与管理研究所，辽宁 大连 116024

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.