[1]孙骞,高岭,刘涛,等.基于熵模型的粒子群优化算法[J].东南大学学报(自然科学版),2019,49(6):1088-1093.[doi:10.3969/j.issn.1001-0505.2019.06.010]
 Sun Qian,Gao Ling,Liu Tao,et al.Particle swarm optimization algorithm based on entropy model[J].Journal of Southeast University (Natural Science Edition),2019,49(6):1088-1093.[doi:10.3969/j.issn.1001-0505.2019.06.010]
点击复制

基于熵模型的粒子群优化算法()
分享到:

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
49
期数:
2019年第6期
页码:
1088-1093
栏目:
计算机科学与工程
出版日期:
2019-11-20

文章信息/Info

Title:
Particle swarm optimization algorithm based on entropy model
作者:
孙骞1高岭2刘涛3姚军3王海1
1西北大学信息科学与技术学院, 西安 710127; 2西安工程大学计算机科学学院, 西安 710600; 3西安科技大学通信与信息工程学院, 西安 710054
Author(s):
Sun Qian1 Gao Ling2 Liu Tao3 Yao Jun3 Wang Hai1
1School of Science Information and Technology, Northwest University, Xi’an 710127, China
2College of Computer Science, Xi’an Polytechnic University, Xi’an 710600, China
3College of Communication and Information Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
关键词:
粒子群优化算法 信息熵模型 求解精度 收敛速度 无效迭代
Keywords:
particle swarm optimization algorithm information entropy model solution accuracy convergence speed invalid iteration
分类号:
TP301.6
DOI:
10.3969/j.issn.1001-0505.2019.06.010
摘要:
为了改善粒子群算法在解决高维优化问题时易早熟收敛且存在大量无效迭代的问题,提出了一种基于熵模型的粒子群优化(EPSO)算法.通过引入信息熵模型,精确分析了粒子群搜索过程中的聚集特性,将粒子群搜索过程划分为3个阶段进行优化.第1阶段引入粒子迭代熵差,优化调整惯性权重;第2阶段根据粒子群熵值变化,适时重置惯性权重;第3阶段采用截断策略,减少粒子群的无效迭代.实验结果表明,在Sphere、Rosenbrock、Ackley、Griewank、Rastrigin五个标准测试函数中,EPSO算法的求解精度和收敛速度都优于传统粒子群算法、经典粒子群算法、自适应惯性权重粒子群算法以及新的自适应惯性权重粒子群算法,并且减少了算法的大量无效迭代,从而验证了EPSO算法的有效性.
Abstract:
To solve the problem that the particle swarm optimization(PSO)algorithm is prone to premature convergence and have a large number of invalid iterations when solving high-dimensional optimization problems, a particle swarm optimization algorithm based on the entropy model(EPSO)is proposed. The information entropy model is introduced to analyze the aggregation characteristics precisely in the process of particle swarm search, and the particle swarm search process is divided into three stages for optimization. In the first stage, the difference of the particle iterative entropy is used to adjust the inertia weight. In the second stage, according to the change of the particle swarm entropy, the inertia weight is reset. In the third stage, the invalid iteration of particle swarm is reduced by truncation strategy. The experimental results show that in five standard test functions such as Sphere, Rosenbrock, Ackley, Griewank, Rastrigin,the solution accuracy and the convergence speed of the EPSO algorithm are higher than those of the traditional PSO algorithm, the classical PSO algorithm, the adaptive inertial weight PSO algorithm and the new adaptive inertia weight PSO algorithm. It also reduces a large number of invalid iterations. The effectiveness of the EPSO algorithm is proved.

参考文献/References:

[1] Eberhart R, Kennedy J. A new optimizer using particle swarm theory[C]// Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Nagoya, Japan, 1995: 39-43. DOI:10.1109/mhs.1995.494215.
[2] Shi Y,Eberhart R. A modified particle swarm optimizer[C]//1998 IEEE International Conference on Evolutionary Computation Proceedings. Anchorage, AK, USA, 1998: 69-73. DOI:10.1109/icec.1998.699146.
[3] Nickabadi A, Ebadzadeh M M, Safabakhsh R. A novel particle swarm optimization algorithm with adaptive inertia weight[J]. Applied Soft Computing, 2011, 11(4): 3658-3670. DOI:10.1016/j.asoc.2011.01.037.
[4] 李学俊, 徐佳, 朱二周, 等. 任务调度算法中新的自适应惯性权重计算方法[J]. 计算机研究与发展, 2016, 53(9): 1990-1999. DOI:10.7544/issn1000-1239.2016.20151175.
Li X J, Xu J, Zhu E Z, et al. A novel computation method for adaptive inertia weight of task scheduling algorithm[J]. Journal of Computer Research and Development, 2016, 53(9): 1990-1999. DOI:10.7544/issn1000-1239.2016.20151175. (in Chinese)
[5] Taherkhani M, Safabakhsh R. A novel stability-based adaptive inertia weight for particle swarm optimization[J]. Applied Soft Computing, 2016, 38: 281-295. DOI:10.1016/j.asoc.2015.10.004.
[6] Tian D P, Shi Z Z. MPSO: Modified particle swarm optimization and its applications[J]. Swarm and Evolutionary Computation, 2018, 41: 49-68. DOI:10.1016/j.swevo.2018.01.011.
[7] 董红斌, 李冬锦, 张小平. 一种动态调整惯性权重的粒子群优化算法[J]. 计算机科学, 2018, 45(2): 98-102, 139. DOI:10.11896/j.issn.1002-137X.2018.02.017.
Dong H B, Li D J, Zhang X P. Particle swarm optimization algorithm with dynamically adjusting inertia weight[J].Computer Science, 2018, 45(2): 98-102, 139. DOI:10.11896/j.issn.1002-137X.2018.02.017. (in Chinese)
[8] 汤可宗, 丰建文, 李芳, 等. 多策略自适应粒子群优化算法[J]. 南京理工大学学报(自然科学版), 2017, 41(3): 301-306, 349. DOI:10.14177/j.cnki.32-1397n.2017.41.03.005.
Tang K Z,Feng J W, Li F, et al. Multi-strategy adaptive particle swarm optimization algorithm[J]. Journal of Nanjing University of Science and Technology, 2017, 41(3): 301-306, 349. DOI:10.14177/j.cnki.32-1397n.2017.41.03.005. (in Chinese)
[9] Liang J, Qin A,Suganthan P N, et al. Comprehensive learning particle swarm optimizer for global optimisation of multimodal functions[J]. IEEE Transactions on Evolutionary Computation, 2006, 10(3): 281-295. DOI:10.1109/TEVC.2005.857610.
[10] Niu B, Huang H L, Tan L J, et al. Symbiosis-based alternative learning multi-swarm particle swarm optimization[J]. ACM Transactions on Computational Biology and Bioinformatics, 2017, 14(1): 4-14. DOI:10.1109/TCBB.2015.2459690.
[11] 耿焕同, 陈正鹏, 陈哲, 等. 基于平衡搜索策略的多目标粒子群优化算法[J]. 模式识别与人工智能, 2017, 30(3): 224-234. DOI:10.16451/j.cnki.issn1003-6059.201703004.
Geng H T, Chen Z P, Chen Z, et al. Multi-objective particle swarm optimization algorithm based on balance search strategy[J]. Pattern Recognition and Artificial Intelligence, 2017, 30(3): 224-234. DOI:10.16451/j.cnki.issn1003-6059.201703004. (in Chinese)
[12] 夏学文, 刘经南, 高柯夫, 等. 具备反向学习和局部学习能力的粒子群算法[J]. 计算机学报, 2015, 38(7): 1397-1407. DOI:10.11897/SP.J.1016.2015.01397.
Xia X W, Liu J N,Gao K F, et al. Particle swarm optimization algorithm with reverse-learning and local-learning behavior[J]. Chinese Journal of Computers, 2015, 38(7): 1397-1407. DOI:10.11897/SP.J.1016.2015.01397. (in Chinese)
[13] 李笠, 王万良, 徐新黎, 等. 基于网格排序的多目标粒子群优化算法[J]. 计算机研究与发展, 2017, 54(5): 1012-1023. DOI:10.7544/issn1000-1239.2017.20160074.
Li L, Wang W L,Xu X L, et al. Multi-objective particle swarm optimization based on grid ranking[J]. Journal of Computer Research and Development, 2017, 54(5): 1012-1023. DOI:10.7544/issn1000-1239.2017.20160074. (in Chinese)
[14] 陈汉武, 朱建锋, 阮越, 等. 带交叉算子的量子粒子群优化算法[J]. 东南大学学报(自然科学版), 2016, 46(1): 23-29. DOI:10.3969/j.issn.1001-0505.2016.01.005.
Chen H W, Zhu J F,Ruan Y, et al. Quantum particle swarm optimization algorithm with crossover operator[J]. Journal of Southeast University(Natural Science Edition), 2016, 46(1): 23-29. DOI:10.3969/j.issn.1001-0505.2016.01.005. (in Chinese)
[15] 李宏光, 廉莹, 方梦琪. 基于熵模型的动态粒子群优化算法[J]. 北京工业大学学报, 2015, 41(5): 657-661. DOI:10.11936/bjutxb2014100042.
Li H G,Lian Y, Fang M Q. Entropy-based dynamic particle swarm optimization algorithm[J]. Journal of Beijing University of Technology, 2015, 41(5): 657-661. DOI:10.11936/bjutxb2014100042. (in Chinese)

相似文献/References:

[1]王冬生,李世华,周杏鹏.基于PSO-RBF神经网络模型的原水水质评价方法及应用[J].东南大学学报(自然科学版),2011,41(5):1019.[doi:10.3969/j.issn.1001-0505.2011.05.024]
 Wang Dongsheng,Li Shihua,Zhou Xingpeng.Assessment method of raw water quality based on PSO-RBF neural network model and its application[J].Journal of Southeast University (Natural Science Edition),2011,41(6):1019.[doi:10.3969/j.issn.1001-0505.2011.05.024]

备注/Memo

备注/Memo:
收稿日期: 2019-03-01.
作者简介: 孙骞(1980—),男,博士,高级工程师, sq@nwu.edu.cn.
基金项目: 国家自然科学基金资助项目(61572401)、赛尔网络下一代互联网技术创新项目(NGII20150403).
引用本文: 孙骞,高岭,刘涛,等.基于熵模型的粒子群优化算法[J].东南大学学报(自然科学版),2019,49(6):1088-1093. DOI:10.3969/j.issn.1001-0505.2019.06.010.
更新日期/Last Update: 2019-11-20