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论文类型：期刊论文

发表时间：2012-04-01

发表刊物：JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS

收录刊物：SCIE、EI、CSCD、Scopus

卷号：23

期号：2

页面范围：265-275

ISSN号：1004-4132

关键字：artificial bee colony (ABC) algorithm; numerical function optimization; swarm intelligence; mutual learning

摘要：The recently invented artificial bee colony (ABC) algorithm is an optimization algorithm based on swarm intelligence that has been used to solve many kinds of numerical function optimization problems. It performs well in most cases, however, there still exists an insufficiency in the ABC algorithm that ignores the fitness of related pairs of individuals in the mechanism of finding a neighboring food source. This paper presents an improved ABC algorithm with mutual learning (MutualABC) that adjusts the produced candidate food source with the higher fitness between two individuals selected by a mutual learning factor. The performance of the improved MutualABC algorithm is tested on a set of benchmark functions and compared with the basic ABC algorithm and some classical versions of improved ABC algorithms. The experimental results show that the MutualABC algorithm with appropriate parameters outperforms other ABC algorithms in most experiments.