一种改进的人工蜂群算法

Improved artificial bee colony algorithm

从经典人工蜂群算法机制出发,针对原始算法在初始种群构造、子种群分组、步长更新和种群淘汰方面的不足进行了改进.新算法运用均匀设计理论构造初始种群,提出了一种种群交叉的Z型分组方法,设计了一种对数函数自适应步长代替原来的随机步长,引入了小生境技术及时淘汰陷入局部最优的个体.实验结果表明,改进后的算法有效地解决了人工蜂群算法早熟收敛、搜索速度较慢等问题,并提高了解的精度.

By analyzing the optimization scheme of the artificial bee colony algorithm, an improved version of such an algorithm is proposed in terms of the initial population construction, subpopulations grouping, step updating and population elimination. The new algorithm constructs the initial population by using the uniform design theory and a Z-type grouping method based on cross population is proposed. Specifically, an adaptive step based on logarithmic functions is designed to replace the original random step. At the same time, the population elimination mechanism based on niche technology is adopted to eliminate these individuals which have fallen into the local optimum in time. Experimental results show that the improved algorithm can avoid premature convergence, accelerate the searching rate and improve the accuracy of the solution.