Development of modified cooperative particle swarm optimization with inertia weight for feature selection

被引：4

作者：

Samigulina, G. ^{[1
]}

Massimkanova, Zh. ^{[2
]}

机构：

[1] Inst Informat & Computat Technol, Lab Intellectual Control Syst & Forecasting, Alma Ata, Kazakhstan

[2] Al Farabi Kazakh Natl Univ, Alma Ata, Kazakhstan

来源：

COGENT ENGINEERING | 2020年 / 7卷 / 01期

关键词：

smart-technology for forecasting and control of complex objects; feature selection; cooperative particle swarm optimization with inertia weight; benchmark datasets; COLONY ALGORITHM;

D O I：

10.1080/23311916.2020.1788876

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The article presents a modified Cooperative Particle Swarm Optimization with Inertia Weight (CPSOIW) for Smart-technology of forecasting and control of complex objects. The software "CPSOIW (Cooperative Particle Swarm Optimization with Inertia Weight)" based on a modified CPSOIW algorithm has been developed in Python programming language and is used to process a multidimensional data and to create an optimal set of descriptors. The proposed algorithm combines the advantages of inertia weight particle swarm optimization (IWPSO) algorithm and cooperative particle swarm optimization (CPSO) algorithm. IWPSO algorithm allows to avoid an early convergence and to prevent particles from trapping into local optima due to update an inertia weight at each iteration. CPSO algorithm explores a search space efficiency and more detailed in a real time by parallel computing of subswarms The modelling results and comparative analysis of CPSOIW and IWPSO algorithms have been performed based on benchmark datasets and a real production data from Installation 300 of Tengizchevroil oil and gas company.

引用

页数：11

共 50 条

[31] Particle swarm optimization and feature selection for intrusion detection system
Kunhare, Nilesh
Tiwari, Ritu
Dhar, Joydip
[J]. SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES, 2020, 45 (01):
[32] An Improved Niching Binary Particle Swarm Optimization For Feature Selection
Dong, Hongbin
Sun, Jing
Li, Tao
Li, Lijie
[J]. 2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2018, : 3571 - 3577
[33] Particle Swarm Optimization Based Feature Selection for Face Recognition
Eleyan, Alaa
[J]. 2019 SEVENTH INTERNATIONAL CONFERENCE ON DIGITAL INFORMATION PROCESSING AND COMMUNICATIONS (ICDIPC 2019), 2019, : 1 - 4
[34] Research on Feature Selection based on Improved Particle Swarm Optimization
Wang, Guo Qing
Jia, Jun Bo
Li, Xu Yuan
[J]. MANUFACTURING ENGINEERING AND AUTOMATION II, PTS 1-3, 2012, 591-593 : 2651 - +
[35] Feature Selection for Alzheimer's Gene Expression Data Using Modified Binary Particle Swarm Optimization
Ramaswamy, Ramya
Kandhasamy, Premalatha
Palaniswamy, Swathypriyadharsini
[J]. IETE JOURNAL OF RESEARCH, 2023, 69 (01) : 9 - 20
[36] A Modified Variable Velocity Strategy Particle Swarm Optimization Algorithm for Multi-objective Feature Selection
Liu, Xikun
Niu, Ben
Yi, Wenjie
[J]. ADVANCES IN SWARM INTELLIGENCE, PT I, ICSI 2024, 2024, 14788 : 46 - 57
[37] New particle swarm optimization for feature selection and classification of microcalcifications in mammograms
Geetha, K.
Thanushkodi, K.
Kumar, A. Kishore
[J]. ICSCN 2008: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING COMMUNICATIONS AND NETWORKING, 2008, : 458 - +
[38] The Improved Particle Swarm Optimization for Feature Selection of Support Vector Machine
Wang, Sipeng
Ding, Sheng
[J]. PROCEEDINGS OF 2017 2ND INTERNATIONAL CONFERENCE ON COMMUNICATION AND INFORMATION SYSTEMS (ICCIS 2017), 2015, : 314 - 317
[39] Particle Swarm Optimization Feature Selection for Breast Cancer Recurrence Prediction
Sakri, Sapiah Binti
Rashid, Nuraini Binti Abdul
Zain, Zuhaira Muhammad
[J]. IEEE ACCESS, 2018, 6 : 29637 - 29647
[40] An enhanced particle swarm optimization with position update for optimal feature selection
Tijjani, Sani
Wahab, Mohd Nadhir Ab
Noor, Mohd Halim Mohd
[J]. EXPERT SYSTEMS WITH APPLICATIONS, 2024, 247

← 1 2 3 4 5 →