一种带搜索因子的全局最优人工蜂群算法

A Gbest-Guided Aritificial Bee Colony Algorithm with Hunting Factor

针对全局最优人工蜂群算法(GABC)搜索迭代过程中未充分考虑到全局优化和局部优化在优化过程中的作用,在一定程度上降低了算法的全局搜索能力,容易陷入局部最优解的问题,提出了一种带搜索因子的全局最优人工蜂群算法(HF-GABC)。在最优人工蜂群(GABC)算法中引入了可以随着优化过程动态搜索的因子,在算法的全局搜索过程和局部搜索过程中进行动态搜索。应用改进的算法对4个标准测试集函数进行仿真试验,并与ABC算法、GABC算法的结果进行比较。实验结果表明:带搜索因子的人工蜂群算法收敛性能优于ABC和GABC算法,有效降低了局部收敛的可能性,并且提高了搜索精度。

Aiming at the problem that the Gbest-guided artificial bee colony （GABC） algorithm is not considered the role of global optimization and local optimization in the optimization process in the iterative process, and is easily to fall into the local optimal solution, a Gbest-guided ABC algorithm based on hunting factor （HF-GABC） is proposed in this paper. In the Gbest-guided artificial bee colony algorithm for numerical function optimization （ GABC ）, the factors that can be dynamically hunted with the optimization process are introduced in the process of global optimization and local search. The simulation experiments were taken on four standard test functions and the improved algorithm were compared with the ABC algorithm and the GABC algorithm. The experimental results show that the convergence rate of HF-ABC algorithm performs better than the ABC and GABC algorithm, which effectively reduces the possibility of local convergence and improves the search precision.