一种双种群差分蜂群算法

A bi-group differential artificial bee colony algorithm

中文关键词  人工蜂群算法  双种群  差分进化算法  反向学习

英文关键词  artificial bee colony  bi-group  differential evolution  opposition-based learning

中文摘要

      人工蜂群算法(ABC)是一种基于蜜蜂群智能搜索行为的随机优化算法. 为了有效改善人工蜂群算法的性能, 结合差分进化算法, 提出一种新的双种群差分蜂群算法(BDABC). 该算法首先通过基于反向学习的策略初始化种群, 使得初始化的个体尽可能均匀分布在搜索空间, 然后将种群中的个体随机分成两组, 每组采用不同的优化策略同时进行寻优, 并通过在两群体之间引入交互学习的思想, 来提高算法的收敛速度. 基于6个标准测试函数的仿真实验表明, BDABC算法能有效避免早熟收敛, 全局优化能力和收敛速率都有显著提高.

英文摘要

      Artificial bee colony(ABC) algorithm is a stochastic optimization algorithm based on the particular intelligent behavior of honeybee swarms. In order to improve the performance of artificial bee colony(ABC) algorithm, a novel bigroup differential artificial bee colony algorithm(BDABC) which is combined with differential evolution(DE) algorithm is proposed. In this algorithm, an initialization strategy based on the opposition-based learning is applied to diversify the initial individuals in the search space. All individuals are randomly divided into two populations, and the evolutions of two sub-groups are simultaneously performed with different optimization strategies. The interactive learning strategy is introduced to accelerate the convergence speed. Experimental results on six benchmark functions show that the BDABC algorithm not only effectively avoids the premature convergence, but also significantly improves the global optimization ability and the convergence speed.