自适应动态控制种群分组的自然计算方法

doi:10.16182/j.issn1004731x.joss.20-FZ0327

系统仿真学报 ›› 2020, Vol. 32 ›› Issue (10): 1884-1894.doi: 10.16182/j.issn1004731x.joss.20-FZ0327

自适应动态控制种群分组的自然计算方法

倪婉璐, 季伟东, 孙小晴

哈尔滨师范大学计算机科学与信息工程学院,黑龙江哈尔滨 150025

收稿日期:2020-03-29 修回日期:2020-06-09 出版日期:2020-10-18 发布日期:2020-10-14
通讯作者: 季伟东(1978-),黑龙江哈尔滨,博士,教授,研究方向为大数据、群体智能。
作者简介:倪婉璐(1996-),女,江苏南通,硕士生,研究方向为群体智能。
基金资助:
国家自然科学基金(31971015),哈尔滨市科技局科技创新人才研究专项(2017RAQXJ050)

Nature Computation of Self-Adaptive Dynamic Control Strategy of Population Grouping

Ni Wanlu, Ji Weidong, Sun Xiaoqing

School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China

Received:2020-03-29 Revised:2020-06-09 Online:2020-10-18 Published:2020-10-14

摘要/Abstract

摘要： 多种群优化方法可以解决数据量增大导致的优化难度增加的问题,现有的种群分组都采用随机分组或人为设定的方法,没有充分考虑粒子运动轨迹。针对此问题,提出一种种群分组自适应动态控制策略,使用高斯拟合函数作为种群分组的参考曲线,根据函数单调区间划分子种群;对于有越过子种群上界趋势的粒子采用逆向策略,保持种群多样性同时提高收敛速度。该策略不依赖于算法的具体进化过程,适用于所有基于种群优化的自然计算方法。验证实验结果表明了所提新算法的有效性和普适性。

关键词: 种群分组, 动态控制, 高斯拟合, 逆向策略, 自然计算

Abstract: Multi-population optimization method can solve the optimization difficulty caused by the increase of data volume, but the existing population grouping is carried out by means of random grouping or artificial setting, which doesn't take particle trajectories into full consideration. In view of the problem a self-adaptive dynamic control strategy of population grouping is proposed, which uses Gaussian fitting function as the reference curve of population grouping and divides sub populations according to the function's monotone interval. For particles with the trend of crossing the upper boundary of sub populations, the contrarian strategy is adopted to maintain the population diversity and improve the convergence speed. The tactics does not rely on concrete evolution procedure of the algorithm, and the strategy is applicable to all nature computation means, based on population optimization. The experimental results show the effectiveness and universality of the algorithm.

Key words: population grouping, dynamic control, gaussian fitting, contrarian strategy, nature computation

中图分类号:

TP301

倪婉璐, 季伟东, 孙小晴. 自适应动态控制种群分组的自然计算方法[J]. 系统仿真学报, 2020, 32(10): 1884-1894.

Ni Wanlu, Ji Weidong, Sun Xiaoqing. Nature Computation of Self-Adaptive Dynamic Control Strategy of Population Grouping[J]. Journal of System Simulation, 2020, 32(10): 1884-1894.

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自适应动态控制种群分组的自然计算方法

Nature Computation of Self-Adaptive Dynamic Control Strategy of Population Grouping

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