双核因素蝙蝠算法

Double Core Factors Bat Algorithm

提出了一种基于蝙蝠算法的新型仿生优化算法——双核因素蝙蝠算法(DCFBA).通过改变蝙蝠算法中的速度更新公式,可有效提高寻优效率.为了验证DCFBA的性能,在9个测试函数上使用标准蝙蝠算法(BA)、粒子群算法(PSO)和DCFBA进行了实验.结果表明:DCFBA在算法的有效性、优越性和稳定性上都优于BA和PSO算法.

In this paper,a new bionic optimization algorithm which termed double core factors bat algorithm(DCFBA)based on the bat algorithm was proposed.A new velocity updating formula was proposed in our algorithm to improve the optimization efficiency.Aimed at showing the advantages of our new algorithm,9 benchmark problems were performed by the classical bat algorithm,the particle swarm optimization and DCFBA respectively.Experimental results show that DCFBA is better than the classical bat algorithm and the particle swarm optimization in effectiveness,superiority and stability.