Journal of System Simulation
Abstract
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.
Recommended Citation
Ni, Wanlu; Ji, Weidong; and Sun, Xiaoqing
(2020)
"Nature Computation of Self-Adaptive Dynamic Control Strategy of Population Grouping,"
Journal of System Simulation: Vol. 32:
Iss.
10, Article 5.
DOI: 10.16182/j.issn1004731x.joss.20-FZ0327
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol32/iss10/5
First Page
1884
Revised Date
2020-06-09
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.20-FZ0327
Last Page
1894
CLC
TP301
Recommended Citation
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.
DOI
10.16182/j.issn1004731x.joss.20-FZ0327
Included in
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Science Commons