关键词: 人工蜂群算法, 群体智能, 数值函数优化, 互学习

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.

Key words: artificial bee colony, swarm intelligence, numerical function optimization, mutual learning