基于子种群的改进人工蜂群算法

针对人工蜂群算法以及现有改进算法的不足，提出了一种基于子种群的改进人工蜂群算法.此算法利用个体适应值与种群适应值平均值的比较，将种群划分为两个子种群，每个子种群采用不同的搜索方式，有效地平衡了不同搜索方式的探索和开发能力.此外，采用分段Logistic方程的初始化方法产生初始解，提高算法的收敛速度.与基本蜂群算法和其他改进蜂群算法进行比较，数值仿真结果表明，所提算法在处理复杂数值优化问题时具有更好的寻优精度和收敛速度.

Modified Artificial Bee Colony Algorithm Based on Sub populations

Due to the shortcomings of the artificial bee colony(ABC)algorithm and the existing improved algorithms， a new modified ABC algorithm was proposed based on the sub populations(SPABC). In this algorithm， the population was divided into two sub populations according to the comparison between the individual fitness value and the mean of population fitness values， and the different search method was adopted in the different sub populations to effectively balance exploration and exploitation capability. In addition， the initial solutions were generated by piecewise Logistic equation to enhance the convergence speed of the algorithm. Compared with ABC algorithm and other modified ABC algorithms， the numerical simulation results demonstrated that the proposed algorithm has better optimization accuracy and convergence speed in solving complex numerical optimization problems.