[1]姜磊、刘建华、张冬阳、卜冠南.一种自适应变异二进制粒子群算法[J].福建工程学院学报,2020,18(03):273-279.[doi:10.3969/j.issn.1672-4348.2020.03.013]
　JIANG Lei,LIU Jianhua,ZHANG Dongyang,et al.An adaptive mutation binary particle swarm optimization algorithm[J].Journal of FuJian University of Technology,2020,18(03):273-279.[doi:10.3969/j.issn.1672-4348.2020.03.013]
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一种自适应变异二进制粒子群算法()
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《福建工程学院学报》[ISSN:2097-3853/CN:35-1351/Z]

卷:
第18卷
期数:
2020年03期
页码:
273-279
栏目:
出版日期:
2020-06-25

文章信息/Info

Title:
An adaptive mutation binary particle swarm optimization algorithm
作者:
姜磊、刘建华、张冬阳、卜冠南
福建工程学院信息科学与工程学院
Author(s):
JIANG Lei;  LIU Jianhua;  ZHANG Dongyang;  BU Guannan
School of Information Science and Engineering, Fujian University of Technology
关键词:
数据预处理; 特征选择; 二进制粒子群算法; 自适应变异; 惯性权重
Keywords:
data preprocessing;  feature selection;  BPSO algorithm;  adaptive mutation;  inertia weight
分类号:
TP301.6
DOI:
10.3969/j.issn.1672-4348.2020.03.013
文献标志码:
A
摘要:
针对二进制粒子群算法(BPSO)具有过早收敛的缺陷，在粒子位置更新后提出变异概率自适应从大到小的变异操作。 同时对算法惯性权重参数采用递增的设置方案，从而得到一种自适应变异BPSO 算法(AMBPSO)，将其应用于特征选择问题。 实验结果表明，提出的新算法前期具有较强的全局搜索能力，后期具有较强的局部搜索能力，能使平均选择特征数量最多从27.6 个减少到20.2 个，平均分类准确率最多从91.346%提升到94.135%。
Abstract:
Aiming at the defect of premature convergence of the binary particle swarm algorithm (BPSO), a mutation operation with the adaptation of the mutation probability going from large to small was proposed after particle position updating. An incremental setting scheme was adopted for the inertia weight parameters of the algorithm to obtain an adaptive mutation BPSO algorithm (AMBPSO), which was applied to the feature selection problem. Experimental results show the proposed new algorithm has strong global search ability in the early stage, and has strong local search ability in the later stage, and it can reduce the average number of selected features from 27.6 to 20.2 and increase the average classification accuracy from 91.346% to 94.135%.

参考文献/References:

[1] 周志华. 机器学习[M]. 北京: 清华大学出版社, 2016: 247-266 .[2] 赵妮, 孙进才, 梁峰, 等. 基于BPSO的水下目标特征选择方法[J]. 计算机仿真, 2008(1): 196-199.[3] 艾解清, 高济, 彭艳斌. 基于离散粒子群算法的织物疵点特征选择[J]. 纺织学报, 2011, 32(11): 53-57.[4] 吴庆涛, 曹继邦, 郑瑞娟, 等. 基于粒子群优化的入侵特征选择算法[J]. 计算机工程与应用, 2013, 49(7): 89-92.[5] 刘建华. 粒子群算法的基本理论及其改进研究[D]. 长沙: 中南大学, 2009. [6] 刘建华, 杨荣华, 孙水华. 离散二进制粒子群算法分析[J]. 南京大学学报(自然科学版), 2011, 47(5): 504-514.[7] 李炜, 巢秀琴. 改进的粒子群算法优化的特征选择方法[J]. 计算机科学与探索, 2019, 13(6): 990-1004.[8] CHUANG L, TSAI S, YANG C. Improved binary particle swarm optimization using catfish effect for feature selection[J]. Expert Systems with Applications, 2011, 38(10): 12699-12707.[9] WEI J, ZHANG R, YU Z, et al. A BPSO-SVM algorithm based on memory renewal and enhanced mutation mechanisms for feature selection[J]. Applied Soft Computing, 2017, 58: 176-192.[10] 张文杰, 蒋烈辉. 一种基于遗传算法优化的大数据特征选择方法[J]. 计算机应用研究, 2020, 37(1): 50-52, 56. [11] SREEJA N K, SANKAR A. Pattern matching based classification using ant colony optimization based featureselection[J]. Applied Soft Computing, 2015, 31: 91-102.[12] 刘亮, 何庆. 基于改进蝗虫优化算法的特征选择方法[J]. 南京大学学报(自然科学版), 2020, 56(1): 41-50.[13] 庄开元. 基于蜻蜓算法和花朵授粉算法的特征选择方法研究[D]. 长春: 吉林大学, 2019.[14] KENNEDY J, EBERHART R. A discrete binary version of the particle swarm algorithm[C]∥1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation. Orlando: IEEE,1997:4104-4109.[15] LIU J, MEI Y, LI X. An analysis of the inertia weight parameter for binary particle swarm o-ptimization[J]. IEEE Transactions on Evolutionary Computation, 2016, 20(5): 666-681.[16] ZHANG Y, WANG S, PHILLIPS P, et al. Binary PSO with mutation operator for feature selection using decision tree applied to spam detection[J]. Knowledge-Based Systems, 2014, 64: 22-31.[17] COVER T, HART P. Nearest neighbor patternclassification[J]. IEEE Transactions on Information Theory, 1967, 13(1): 21-27.

更新日期/Last Update: 2020-06-25