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中文摘要：
针对基本人工蜂群算法存在局部搜索能力差、收敛速度慢等缺点,提出一种动态调整子种群个体数目的改进人工蜂群算法用于求解无约束优化问题.该算法利用反向学习策略产生初始种群,以保证个体尽可能均匀分布在搜索空间中;基于个体适应度值,将种群分为两个子种群,分别采取不同的蜜源搜索公式,用于进行全局搜索和局部搜索.5个标准测试函数的仿真实验结果表明,改进算法具有较好的寻优性能.
英文摘要：
Aimed at the defects of poor global searching ability and slow convergence in artificial bee colony（ABC）algorithm,an improved artificial bee colony algorithm for dynamical adjustment of the number of individuals in sub-population is proposed to deal with the unconstrained optimization problem.In this algorithm,an initial population is generated based on reversed learning strategy to assure that the individuals are distributed in the search space as uniformly as possible.The population is divided into two sub-populations based on the fitness values of the individuals and different honey source searching formulae are selected to conduct global and local search,respectively.Experimental results of five benchmark function simulation show that the improved algorithm has better optimization performance.
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