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

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

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.

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

CLC Number:

TP301

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.

References 18

[1]	王蓉芳, 焦李成, 刘芳, 等. 自适应动态控制种群规模的自然计算方法[J]. 软件学报, 2012, 23(7): 1760-1772.Wang Rongfang, Jiao Licheng, Liu Fang, et al.Nature Computation with Self-Adaptive Dynamic Control Strategy of Population Size[J]. Journal of Software, 2012, 23(7): 1760-1772.
[2]	康琦, 安静, 汪镭, 等. 自然计算的研究综述[J].电子学报, 2012, 40(3): 548-558.Kang Qi, An Jing, Wang Lei, et al.Nature-Inspired Computation: A Survey[J]. Acta Electronica Sinica, 2012, 40(3): 548-558.
[3]	吴浩扬, 常炳国, 朱长纯, 等. 基于模拟退火机制的多种群并行遗传算法[J]. 软件学报, 2000, 11(3): 416-420.Wu Haoyang, Chang Bingguo, Zhu Changchun, et al.A Multigroup Parallel Genetic Algorithm Based on Simulated Annealing Method[J]. Journal of Software, 2000, 11(3): 416-420.
[4]	郭观七, 喻寿益, 贺素良. 自适应小生态遗传算法的理论分析和加速技术[J]. 计算机学报, 2003, 26(6): 753-758.Guo Guanqi, Yu Shouyi, He Suliang.Theoretic analysis and accelerating of a class of self-adaptive niching genetic algorithms[J]. Chinese Journal of Computers, 2003, 26(6): 753-758.
[5]	周丹, 谢敏, 刘方, 等. 基于精英协同的多种群分布估计算法[J]. 计算机应用与软件, 2017, 34(1): 281-285.Zhou Dan, Xie Min, Liu Fang, et al.Multi-population estimation of distribution algorithm based on elite cooperation[J]. Computer Applications and Software, 2017, 34(1): 281-285.
[6]	邓先礼, 魏波, 曾辉, 等. 基于多种群的自适应迁移PSO算法[J]. 电子学报, 2018, 46(8): 1858-1865.Deng Xianli, Wei Bo, Zeng Hui, et al.Multi-population based self-adaptive migration PSO[J]. Acta Electronica Sinica, 2018, 46(8): 1858-1865.
[7]	杜永兆, 范宇凌, 柳培忠, 等. 多种群协方差学习差分进化算法[J]. 电子与信息学报, 2019, 41(6): 1488-1495.Du Yongzhao, Fan Yuling, Liu Peizhong, et al.Multi-populations covariance learning differential evolution algorithm[J]. Journal of Electronics and Information Technology, 2019, 41(6): 1488-1495.
[8]	Clerc M, Kennedy J.The particle swarm-explosion, stability, and convergence in a multidimensional complex space[J]. IEEE Transactions on Evolutionary Computation (S1089-778X), 2002, 6(1): 58-73.
[9]	J Kennedy.Bare bones particle swarms[C].Proceedings of the 2003 IEEE Swarm Intelligence Symposium. NJ: IEEE, 2003: 80-87.
[10]	王东风, 孟丽, 赵文杰. 基于自适应搜索中心的骨干粒子群算法[J]. 计算机学报, 2016, 39(12): 2652-2667.Wang Dongfeng, Meng Li, Zhao Wenjie.Improved bare bones particle swarm optimization with Adaptive search center[J]. Chinese Journal of Computers, 2016, 39(12): 2652-2667.
[11]	Wang L, He L, Mishra A, Li C M.Active contours driven by local Gaussian distribution fitting energy[J]. Signal Processing (S0165-1684), 2009, 89(12): 2435-2447.
[12]	高雅, 周佳霖, 侯雪, 等. 基于高斯拟合的高光谱影像配准算法[J]. 红外与激光工程, 2016, 45(增2): 131-136.Gao Ya, Zhou Jialin, Hou Xue, et al.Registration algorithm for hyperspectral image based on Gaussian fitting[J]. Infrared and Laser Engineering, 2016, 45(S2): 131-136.
[13]	陈海鹏, 申铉京, 龙建武. 采用高斯拟合的全局阈值算法阈值优化框架[J]. 计算机研究与发展, 2016, 53(4): 892-903.Chen Haipeng, Shen Xuanjing, Long Jianwu.Threshold optimization framework of global thresholding algorithms using gaussian fitting[J]. Journal of Computer Research and Development, 2016, 53(4): 892-903.
[14]	Cheng Song, Yuyao He, Branko Ristic, et al.Collaborative info taxis: Searching for a signal-emitting source based on particle filter and Gaussian fitting[J]. Robotics and Autonomous Systems (S0921-8890), 2020, 125(C):1-11.
[15]	周新宇, 吴志健, 王晖, 等. 一种精英反向学习的粒子群优化算法[J]. 电子学报, 2013, 41(8): 1647-1652.Zhou Xinyu, Wu Zhijian, Wang Hui, et al.Elite opposition-based particle swarm optimization[J]. Acta Electronica Sinica, 2013, 41(8): 1647-1652.
[16]	罗强, 季伟东, 徐浩天, 等. 一种最优粒子逐维变异的粒子群优化算法[J]. 小型微型计算机系统, 2020, 41(2): 259-263.Luo Qiang, Ji Weidong, Xu Haotian, et al.Particle swarm optimization with global best particle dimension- by-simension mutation[J]. Journal of Chinese Computer Systems, 2020, 41(2): 259-263.
[17]	Shi Y, Eberhart R C.A modified particle swarm optimizer[C]. Proceedings of the IEEE International Conference on Evolutionary Computation. Piscataway, NJ, Anchorage, AK USA: IEEE service center, 1998: 69-73.
[18]	Holland J H.Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence[D]. Michigan, USA: University of Michigan Press, 1975.