Adaptive Gravitational Search Algorithm Based on DBSCAN

QIN Ling; TAO Wen-wen

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Adaptive Gravitational Search Algorithm Based on DBSCAN

更新时间：2026-09-04

- Adaptive Gravitational Search Algorithm Based on DBSCAN
- Vol. 33, Issue 12, Pages: 62-65(2016)
- 作者机构：
  
  南京工业大学机构计算机科学与技术学院,江苏,南京,211816
- 作者简介：
- 基金信息：
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- Published：2016
- 稿件说明：
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秦岭, 陶文雯. 基于DBSCAN的自适应GSA算法研究[J]. 微电子学与计算机, 2016, 33(12): 62-65, 70.

QIN Ling, TAO Wen-wen. Adaptive Gravitational Search Algorithm Based on DBSCAN[J]. 2016, 33(12): 62-65.
秦岭, 陶文雯. 基于DBSCAN的自适应GSA算法研究[J]. 微电子学与计算机, 2016, 33(12): 62-65, 70. DOI：

QIN Ling, TAO Wen-wen. Adaptive Gravitational Search Algorithm Based on DBSCAN[J]. 2016, 33(12): 62-65. DOI：

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.

Abstract

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. Whats 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.

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references

Daniel J, Christian B, Thomas C. Analysis of Constraint Handling Methods for the Gravitational Search Algorithm[C]// Proceedings of the IEEE Congress on Evolutionary Computation. Beijing, China: IEEE, 2014.

Sombra A, Valdez F, Melin P, et al. A new gravitational search algorithm using fuzzy logic to parameter adaptation[C]//IEEE Congress on Evolutionary Computation. Cancun, Mexico: IEEE, 2013: 1068-1074.

Huiqin C, Sheng L, Zheng T, Hybrid Gravitational Search Algorithm with Random-key Encoding. Scheme Combined with Simulated Annealing[J]. International Journal of Computer Science and Network Security, 2011, 11(6): 208-217. DOI: 208

Shams M, Rashedi E, Hakimi A. Clustered-gravitational search algorithm and its application in parameter optimization of a low noise amplifier[J]. Applied Mathematics % Computation, 2015, 258(6): 436-453. DOI: 436

Amoozegar M, Rashedi E. Parameter tuning of GSA using DOE[C]// Computer and Knowledge Engineering(CCKE). Mashhad, Iran. 2014 4th International Econference on. IEEE, 2014: 431-436.

赖丽萍, 聂瑞华, 汪疆平, 等. 基于MapReduce的改进DBSCAN算法[J]. 计算机科学, 2015, 9(S2): 396-399. DOI: 396

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