基于伊藤算法的改进人工蜂群算法

Artificial Bee Colony Algorithm Based on Ito Algorithm

针对人工蜂群算法(ABC)在求解复杂问题时出现的收敛速度慢、易陷入局部最优的缺点,在布朗运动和伊藤随机过程的启示下,借鉴伊藤算法的设计思想,提出了一种基于布朗运动的改进人工蜂群优化算法(BMABC)。在采蜜蜂和观察蜂阶段分别设计了不同的漂移算子和波动算子。漂移算子保证算法向着最优解的位置漂移,波动算子保证了解的多样性。分别使用ABC、GABC和BMABC对5个经典函数进行了测试。实验结果表明,BMABC算法具有收敛速度快、收敛精度高的特点,并具有良好的稳定性。

When resolving complex problems,the artificial bee colony(ABC)has some disadvantages of slow convergence rate and easy to fall into local optimization,with the inspiration of the Brownian motion and Ito process,and imitating the designed idea of Ito algorithm,this paper proposed a improved artificial bee colony based on Ito algorithm(BMABC).We designed different drift operator and fluctuation operator in the phases of the employed bees and the onlookers respectively.The drift operator ensures the drift direction to the optimal solution.The fluctuation operator ensures the diversity of the solutions.ABC,GABC and BMABC were tested by five classic functions.Experimental results show that BMABC retains the fast convergence and high convergence precision characteristics,as well as better stability.