Abstract The multi-strategy mechanism is an effective way to improve the performance of artificial bee colony algorithm(ABC). However, characteristics of different individuals in the population are not considered in the existing methods, and the strategies are typically assigned to individuals without distinction. Consequently, the effectiveness of the multi-strategy mechanism is limited. Therefore, a multi-strategy ABC algorithm based on fitness grouping is proposed in this paper with consideration of both excellent individuals and poor individuals. Firstly, the population is divided into three groups according to fitness value of the individuals. Thus, the individuals of each group hold their own characteristics and preferences for exploration or exploitation. Then, solution search equations with distinct search capabilities are designed for three groups respectively to achieve division and cooperation among the groups and balance exploration and exploitation of the whole population. Finally, a solution search equation integrating the global best individual and some elite individuals is specially designed to further maintain the original role of the onlooker bee phase. In this scenario, the superior individuals can guide the search procedure. Experimental results on CEC2013 and CEC2015 datasets indicate the strong competitiveness of the proposed algorithm.
Fund:National Natural Science Foundation of China(No.61966019,61876074,61866017), Natural Science Foundation of Jiangxi Province(No.20192BAB207030), Postgraduate Innovation Fund of Education Department of Jiangxi Province(No.YC2021-S309)
Corresponding Authors:WANG Mingwen, Ph.D., professor. His research interests include evolutionary computation and its applications, Chinese information processing, information retrieval and machine lear-ning
About author:: ZHOU Xinyu, Ph.D., associate profe-ssor. His research interests include evolutio-nary computation and its applications. HU Jiancheng, master student. His research interests include evolutionary computation and its applications. WU Yanlin, master. His research interests include evolutionary computation and its app-lications. ZHONG Maosheng, Ph.D., professor. His research interests include evolutionary computation and its applications, machine learning, data mining(big data), and natural language processing.
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2022, Vol. 35 
