基于适应度分组的多策略人工蜂群算法

A Multi-strategy Artificial Bee Colony Algorithm Based on Fitness Grouping

多策略机制是改进人工蜂群算法的有效手段,但是现有的很多相关工作未考虑种群中不同个体的特点,一视同仁地分配解搜索方程,导致多策略机制的有效性受到限制.为此,文中提出基于适应度分组的多策略人工蜂群算法,既考虑种群中的优秀个体,又照顾较差个体.首先,根据个体适应度把种群划分为三组,每组个体都有自己的特点,能在勘探和开采之间有所侧重.然后,为每组设计具备不同搜索能力的解搜索方程,使各组能相互分工与合作,更好地平衡整体种群的勘探和开采能力.最后,为了继续维持观察蜂阶段的原有作用,设计融合全局最优个体和精英个体的解搜索方程,充分发挥优秀个体在搜索过程中的引导作用.在CEC2013、CEC2015测试集上的实验表明文中算法竞争力较强.

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.