[1]徐浩天,季伟东,孙小晴,等.基于正态分布衰减惯性权重的粒子群优化算法[J].深圳大学学报理工版,2020,37(2):208-213.[doi:10.3724/SP.J.1249.2020.02208]
 XU Haotian,JI Weidong,SUN Xiaoqing,et al.A PSO algorithm with inertia weight decay by normal distribution[J].Journal of Shenzhen University Science and Engineering,2020,37(2):208-213.[doi:10.3724/SP.J.1249.2020.02208]
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基于正态分布衰减惯性权重的粒子群优化算法()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第37卷
期数:
2020年第2期
页码:
208-213
栏目:
电子与信息科学
出版日期:
2020-03-16

文章信息/Info

Title:
A PSO algorithm with inertia weight decay by normal distribution
文章编号:
202002014
作者:
徐浩天季伟东孙小晴罗强
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
Author(s):
XU Haotian JI Weidong SUN Xiaoqing and LUO Qiang
School of Computer Science and Information Engineering,Harbin Normal University, Harbin 150025, Heilongjiang Province, P.R.China
关键词:
人工智能群体智能算法粒子群算法惯性权重正态分布衰减策略
Keywords:
artificial intelligence swarm intelligence algorithm particle swarm optimization inertia weight normal distribution attenuation strategy
分类号:
TP391
DOI:
10.3724/SP.J.1249.2020.02208
文献标志码:
A
摘要:
针对粒子群优化(particle swarm optimization, PSO)算法无法在提高收敛速度的同时避免早熟的缺陷,提出基于正态分布衰减惯性权重粒子群优化(normal distribution decay inertial weight particle swarm optimization, NDPSO)算法.以正态分布曲线作为惯性权重的衰减策略曲线,通过引入控制因子对粒子的位置进行改善,使得NDPSO算法能很好的在优化过程中平衡全局搜索和局部搜索能力.使用8个标准函数测试分别对粒子群优化(particle swarm optimization, PSO)、线性权重衰减粒子群优化(linear decay inertial weight particle swarm optimization, LDWPSO)、指数权重衰减粒子群优化(exponential decay weight particle swarm optimization, EXPPSO)、收缩因子粒子群优化(constriction factor particle swarm optimization, CFPSO)、高斯分布衰减惯性权重粒子群优化(Gaussian decay inertial weight particle swarm optimization, GDIWPSO)、基于动态加速度系数的粒子群优化(particle swarm optimization based on dynamic acceleration coefficients, PSO-DAC)、性权重自适应粒子群优化(inertia weight adaptive particle swarm optimization, 简称PSO-LH)算法以及NDPSO算法进行仿真,分析他们的收敛速度和收敛精度.结果表明,NDPSO算法不管在单峰函数问题还是多峰函数问题上,总体性能都优于其他算法.
Abstract:
To avoid early maturity stagnation while increasing convergence speed of particle swarm optimization (PSO) algorithm, a normal distribution decay inertial weight particle swarm optimization (NDPSO) algorithm is proposed based on the inertia weight of which the decay strategy curve is normal distribution curve. By introducing the control factor to improve the position of the particle, the NDPSO algorithm can balance the global search and local search ability in the optimization process. The PSO, linear decay inertial weight particle swarm optimization (LDWPSO), exponential decay weight particle swarm optimization (EXPPSO), constriction factor particle swarm optimization (CFPSO), Gaussian decay inertial weight particle swarm optimization (GDIWPSO), particle swarm optimization based on dynamic acceleration coefficients (PSO-DAC), inertia weight adaptive particle swarm optimization (PSO-LH) and NDPSO algorithms are simulated using 8 standard function test evaluations, and their convergence speed and convergence precision are analyzed. The results show that the NDPSO algorithm outperforms other algorithms in terms of single-peak function or multi-peak function.

参考文献/References:

[1] KENNDY J, EBERHART R C. Particle swarm optimization[C]// Proceedings of 4th IEEE International Conference on Neural Networks. Perth,Australia:IEEE, 1995: 1942-1948.
[2] GOLDBERG D E. Genetic algorithms in search optimization and machine learning[M]. Reading Mass, USA: Addison-Welsey, 1989: 95-99.
[3] JI Weidong, WANG Jianhua, ZHANG Jun. An improved real hybrid genetic algorithm[J]. Tehnicki Vjesnik-Tehnical Gazette, 2014, 21(5): 979-986.
[4] DORIGO, MANIEZZO M, COLORIN V. A ant system: optimization by a colony of cooperating agents[J]. IEEE Transactions on SMC, 1996, 26(1): 8-41.
[5] KARABOGA N. A new design method based on artificial bee colony algorithm for digital IIR filters[J]. Journal of the Franklin Institute-engineering and Applied Mathematics, 2009, 346(4):328-348.
[6] PANG Hui, LIU Fan, XU Zeren. Variable universe fuzzy control for vehicle semi-active suspension system with MR damper combining fuzzy neural network and particle swarm optimization[J]. Neurocomputing, 2018, 306:10-140.
[7] KENNEDY J. Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance[C]// Proceedings of IEEE Congress on Evolutionary Computation. Piscataway, USA: IEEE, 1999: 1931-1938.
[8] KENNEDY J, Mendes R. Population structure and particle swarm performance[C]// Proceedings of IEEE Congress on Computational Intelligence. Hawaii, USA: IEEE, 2002: 1671-1676.
[9] VEDTERDTROM J S, RIGET J, KRINK T. Division of labor in particle swarm optimization[C]// Proceedings of the Congress on Evolutionary Computation. Honolulu, USA: IEEE, 2002: 1570-1575.
[10] BERGH F V D, ENGELBRECHT A P. A cooperative approach to particle swarm optimization[C]// Proceedings of IEEE Transactions on Evolutionary Computation. Portland, USA: IEEE, 2004: 225-239.
[11] DENG Xianli, WEI Bo, ZENG Hui, et al. A multi-population based self-adaptive migration PSO[J]. Acta Electronica Sinica, 2018, 46(8): 1858-1865.
[12] SHI Y, EBERHART R C. A modified particle swarm optimizer[C]// Proceedings of IEEE Congress on Evolutionary Computation. Piscataway, USA: IEEE, 1998: 69-73.
[13] YAN Chunman, LU Genyuan, LIU Yingting. et al. A modified PSO algorithm with exponential decay weight[C]// Proceedings of the 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery. Changsha, China: IEEE, 2018: 239-242.
[14] CLERC M, KENNEDY J. The particle swarm-explosion, stability and convergence in a multidimensional complex space[J]. Proceedings of IEEE Transactions on Evolutionary Computation, 2002, 6(1): 58-73.
[15] ZHANG Xun, WANG Ping. Particle swarm optimization algorithm based on Gaussian function decreasing inertia weight[J]. Application Research of Computers, 2012, 29(10): 3710-3724.
[16] ZHOU Lingyun, DING Lingxin, PENG Hu. et al. Neighborhood centroid opposition-based particle swarm optimization[J]. Acta Electroica Snicia, 2017, 45(11): 2815-2823.
[17] SONG Huajun, LIU Feng, CHEN Haihua. et al. A stochastic maximum likelihood algorithm based on improved PSO[J]. Acta Electroica Sinica, 2017, 45(8): 1990-1994.
[18] 滕志军,吕金玲,郭力文,等.基于动态加速因子的粒子群优化算法研究[J].微电子学与计算机,2017,34(12):125-129.
TENG Zhijun, LV(Lyn) Jinling, GUO Liwen, et al. Research on particle swarm optimization based on dynamic acceleration coefficients[J]. Microelectronics & Computer, 2017, 34(12): 125-129.(in Chinese)
[19] LUO Hua. An inertia weight adaptive particle swarm optimization algorithm[J]. Electronic Science and Technology, 2017, 30(2): 30-36.

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备注/Memo

备注/Memo:
Received:2019-04-06;Accepted:2019-05-27
Foundation:National Natural Science Foundation of China (31971015); Harbin Science and Technology Bureau Special Fund for Scientific and Technological Innovation Research (2017RAQXJ050); Innovation Research Project of Master’s Degree in Harbin Normal University (HSDSSCX2019-08)
Corresponding author:Professor JI Weidong. E-mail: Kingjwd@126.com
Citation:XU Haotian, JI Weidong. SUN Xiaoqing, et al. A PSO algorithm with inertia weight decay by normal distribution[J]. Journal of Shenzhen University Science and Engineering, 2020, 37(2): 208-213.(in Chinese)
基金项目:国家自然科学基金资助项目(31971015);哈尔滨市科技局科技创新人才研究专项资助项目(2017RAQXJ050);哈尔滨师范大学硕士研究生创新科研资助项目(HSDSSCX2019-08)
作者简介:徐浩天(1996—),哈尔滨师范大学硕士研究生.研究方向:群体智能算法.E-mail: 15189871428@163.com
引文:徐浩天,季伟东.孙小晴,等.基于正态分布衰减惯性权重的粒子群优化算法[J]. 深圳大学学报理工版,2020,37(2):208-213.
更新日期/Last Update: 2020-03-30