Improved artificial bee colony algorithm with mutual learning  

作　　者：Yu Liu Xiaoxi Ling Yu Liang Guanghao Liu 

机构地区：[1]School of Software, Dalian University of Technology, Dalian 116024, P. R. China [2]Civil Aviation Flight University of China, Guanghan 618307, E R. China

出　　处：《Journal of Systems Engineering and Electronics》2012年第2期265-275,共11页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China (60803074);the Fundamental Research Funds for the Central Universities (DUT10JR06)

摘　　要：The recently invented artificial bee colony （ABC） al- gorithm 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 find- ing 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 perfor- mance of the improved MutualABC algorithm is tested on a set of benchmark functions and compared with the basic ABC algo- rithm 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.The recently invented artificial bee colony （ABC） al- gorithm 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 find- ing 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 perfor- mance of the improved MutualABC algorithm is tested on a set of benchmark functions and compared with the basic ABC algo- rithm 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.

关 键 词：artificial bee colony （ABC） algorithm numerical func- tion optimization swarm intelligence mutual learning. 

分 类 号：O224[理学—运筹学与控制论] TN958.2[理学—数学]