Opposition-Based Bare Bone Particle Swarm Optimization

被引：1

作者：

Chen, Chang-Huang ^{[1
]}

机构：

[1] Tungnan Univ, Dept Elect Engn, New Taipei City 222, Taiwan

来源：

PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON INTELLIGENT TECHNOLOGIES AND ENGINEERING SYSTEMS (ICITES2013) | 2014年 / 293卷

关键词：

Bare bone particle swarm; Opposite number; Opposition-based learning; Particle swarm optimization;

D O I：

10.1007/978-3-319-04573-3_137

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Bare bone particle swarm optimization (BPSO) is a simple approach for solving optimization problem. However, this population-based algorithm also suffers premature problem for some complex problems, especial for high-order dimensional, nonlinear problems. This paper presents a new approach to enhance BPSO's searching capability. The proposed opposition-based bare bone particle swarm optimization (OBPSO) employs opposition learning strategy to extend the exploration capability such that avoiding get stuck on local optimum. A set of six benchmark functions is applied for numerical verification. Experimental results confirm the strength of the proposed approach, based on comparison with PSO and original OBPSO. It is seen that OBPSO outperforms PSO and BPSO both in solution accuracy and convergent rate.

引用

页码：1125 / 1132

页数：8

共 50 条

[1] Elite opposition-based particle swarm optimization
Zhou, X.-Y. (xyzhou@whu.edu.cn), 1647, Chinese Institute of Electronics (41): : 1647 - 1652
[2] An Opposition-based Particle Swarm Optimization Algorithm for Noisy Environments
Xiong, Caifei
Kang, Qi
Zhao, Zeyu
Zhou, MengChu
2018 IEEE 15TH INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC), 2018,
[3] Integrating opposition-based learning into the evolution equation of bare-bones particle swarm optimization
Hao Liu
Gang Xu
Guiyan Ding
Dawei Li
Soft Computing, 2015, 19 : 2813 - 2836
[4] Adaptive Mutation Opposition-Based Particle Swarm Optimization
Kang, Lanlan
Dong, Wenyong
Li, Kangshun
COMPUTATIONAL INTELLIGENCE AND INTELLIGENT SYSTEMS, (ISICA 2015), 2016, 575 : 116 - 128
[5] Integrating opposition-based learning into the evolution equation of bare-bones particle swarm optimization
Liu, Hao
Xu, Gang
Ding, Guiyan
Li, Dawei
SOFT COMPUTING, 2015, 19 (10) : 2813 - 2836
[6] Opposition-Based Hybrid Strategy for Particle Swarm Optimization in Noisy Environments
Kang, Qi
Xiong, Caifei
Zhou, Mengchu
Meng, Lingpeng
IEEE ACCESS, 2018, 6 : 21888 - 21900
[7] Hybrid Mean Center Opposition-Based Learning Particle Swarm Optimization
Sun H.
Deng Z.-C.
Zhao J.
Wang H.
Xie H.-H.
Tien Tzu Hsueh Pao/Acta Electronica Sinica, 2019, 47 (09): : 1809 - 1818
[8] Adaptive Opposition-Based Particle Swarm Optimization Algorithm and Application Research
Ma, Y. Y.
Jin, H. B.
Li, H.
Zhang, H.
Li, J.
2019 IEEE 4TH INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING (ICSIP 2019), 2019, : 518 - 523
[9] Opposition-based particle swarm optimization with adaptive mutation strategy
Wenyong Dong
Lanlan Kang
Wensheng Zhang
Soft Computing, 2017, 21 : 5081 - 5090
[10] Opposition-based particle swarm optimization with adaptive mutation strategy
Dong, Wenyong
Kang, Lanlan
Zhang, Wensheng
SOFT COMPUTING, 2017, 21 (17) : 5081 - 5090

← 1 2 3 4 5 →