基于多维贪婪搜索的人工蜂群算法

Artificial Bee Colony Algorithm Based on Multi-dimensional Greedy Search

人工蜂群算法在多峰高维函数优化问题的求解上取得了较好的结果,但随着函数的复杂度及维数增高,仍存在收敛速度慢、易陷入局部最优等问题。为此,提出一种新的人工蜂群算法。将人工蜂群对食物源的单维贪婪搜索改进为多维贪婪搜索以增强蜂群的搜索能力,避免在个别维度上出现较优解的食物源由于达到更新阈值却被废弃而造成迂回搜索的现象,引入扰动搜索机制避免迭代后期食物源位置在个别维度收敛导致算法陷入局部最优。仿真实验结果表明,该算法能保持深度挖掘和广度搜索上的平衡,在高维函数优化问题求解的收敛速度和计算精度方面表现出较好的性能。

Artificial Bee Colony （ ABC ） algorithm can be efficiently employed to solve the multimodal and high dimensional function optimization problem. However,low search speed and premature convergence frequently appear with more complex problem. In order to improve the algorithm performance,this paper proposes a new artifciall bee colony algorithm . It introduces a search equation based on multi-dimensional greedy search to enhance local search and avoid the solution to be abandoned which achieves optimum value in some dimensions but reach the maximum update limit. New algorithm also adds a disturbance mechanism to avoid obtaining partial optimal solutions when premature convergence in a few dimensions. Experimental results show the new algorithm can balance the exploitation and exploration,has more fast convergence speed and better computational precision in solving the multimodal and high dimensional function optimization problem.