基于DBSCAN的自适应GSA算法研究  

Adaptive Gravitational Search Algorithm Based on DBSCAN

作　　者：秦岭[1] 陶文雯 

机构地区：[1]南京工业大学计算机科学与技术学院,江苏南京211816

出　　处：《微电子学与计算机》2016年第12期62-65,70,共5页Microelectronics & Computer

摘　　要：在万有引力搜索算法(GSA)的基础上,提出了基于DBSCAN的自适应万有引力搜索算法(DBAGSA).算法在首次迭代过程中先通过聚类将种群划分对初始种群进行预处理,然后各子群中的个体再根据其适应度值和引力常数更新自己的速度、位置和引力质量值,并以自适应的方式更新最优解的信息.通过聚类和自适应的方式提高万有引力搜索算法的搜索能力和收敛速度,并有效平衡其群居探测能力及局部搜索能力.实验结果表明,改进后的万有引力搜索算法与标准GSA算法相比,收敛速度更快而且收敛精度更高.In this paper, we provide an adaptive gravitational search algorithm based on DBSCAN. During iterations the initial population is clustered into certain number of groups, and then individuals in subgroups update velocity, position and mass values by their own fitness and gravitational constant values. Individuals adaptively update their information. Clustering and adaption are adopted to improve global search ability and convergence rate. What＇s more, it provides effective balance between exploration and exploitation. Experimental results show that better performance has been achieved with high convergence rate and better convergence accuracy by using the improved GSA.

关 键 词：GSA算法 DBSCAN 自适应策略 启发式优化算法 

分 类 号：TP31[自动化与计算机技术—计算机软件与理论]