基于梯度粒子群算法的细菌觅食算法

摘要

To overcome the drawbacks of bacterial foraging algorithm for the optimization process, that was the weak ability to perceive the environment and vulnerable to perception of local extreme, this article merged the idea of GPSO algorithm into the bacterial foraging to improve the speed and convergence capabilities of BFA. According to this, it presented a bacterial foraging algorithm based on gradient particle swarm optimization ( GPSO-BFA). The presented hybrid method incorporated the advantages of the excellent global searching of the BFA and the local speedy convergence of the gradient method. Simulation results on six Benchmark functions show that the proposed algorithm is superior to the other 4 kinds of bacterial foraging algorithm.%针对细菌觅食算法在优化过程中环境感知能力较弱且容易陷入局部极值的缺陷,将梯度粒子群算法的基本思想引入细菌觅食算法中,改进原算法的收敛速度和收敛能力,并据此提出了基于梯度粒子群算法的细菌觅食算法GPSO-BFA.该算法既利用了细菌觅食算法出色的全局搜索能力,又借助梯度粒子群算法的快速局部寻优能力,很好地将两者的优势结合在一起.基于六个高维Benchmark函数的实验结果显示,该算法在收敛速度和精度方面都优于其他四种细菌觅食算法.