Reliability optimization allocation method based on improved dynamic particle swarm optimization

Qihai Liang; Zhe Wang; Jinzhu Qu; Xiaojian Yi

doi:10.1109/SDPC.2019.00084

Reliability optimization allocation method based on improved dynamic particle swarm optimization

Qihai Liang, Zhe Wang, Jinzhu Qu, Xiaojian Yi^*

^*Corresponding author for this work

School of Mechatronical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Aiming at the reliability optimization allocation, the basic particle swarm optimization algorithm has the disadvantages of slow convergence and easy to fall into local extremum. This paper proposes an improved dynamic particle swarm optimization algorithm, which uses the extrapolation technique in mathematics to guide the evolution direction of particles. At the same time, the idea of transforming the multimodal function and dynamically adjusting the group size is introduced, and the elite set is used to preserve the optimal individual of each generation. Finally, this paper uses a certain type of sonar as an example to establish a reliability mathematical model, which uses the basic particle swarm optimization algorithm and the improved particle swarm optimization algorithm proposed in this paper. The results show that the proposed method has a stronger search ability, higher accuracy and better stability.

Original language	English
Title of host publication	Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019
Editors	Chuan Li, Shaohui Zhang, Jianyu Long, Diego Cabrera, Ping Ding
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	429-434
Number of pages	6
ISBN (Electronic)	9781728101996
DOIs	https://doi.org/10.1109/SDPC.2019.00084
Publication status	Published - Aug 2019
Event	2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019 - Beijing, China Duration: 15 Aug 2019 → 17 Aug 2019

Publication series

Name	Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019

Conference

Conference	2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019
Country/Territory	China
City	Beijing
Period	15/08/19 → 17/08/19

Keywords

Optimization allocation
Reliability
Swarm optimization algorithm

Access to Document

10.1109/SDPC.2019.00084

Cite this

Liang, Q., Wang, Z., Qu, J., & Yi, X. (2019). Reliability optimization allocation method based on improved dynamic particle swarm optimization. In C. Li, S. Zhang, J. Long, D. Cabrera, & P. Ding (Eds.), Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019 (pp. 429-434). Article 9168924 (Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SDPC.2019.00084

Liang, Qihai ; Wang, Zhe ; Qu, Jinzhu et al. / Reliability optimization allocation method based on improved dynamic particle swarm optimization. Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019. editor / Chuan Li ; Shaohui Zhang ; Jianyu Long ; Diego Cabrera ; Ping Ding. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 429-434 (Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019).

@inproceedings{35e910edf8ef478b8d535ea85c22085b,

title = "Reliability optimization allocation method based on improved dynamic particle swarm optimization",

abstract = "Aiming at the reliability optimization allocation, the basic particle swarm optimization algorithm has the disadvantages of slow convergence and easy to fall into local extremum. This paper proposes an improved dynamic particle swarm optimization algorithm, which uses the extrapolation technique in mathematics to guide the evolution direction of particles. At the same time, the idea of transforming the multimodal function and dynamically adjusting the group size is introduced, and the elite set is used to preserve the optimal individual of each generation. Finally, this paper uses a certain type of sonar as an example to establish a reliability mathematical model, which uses the basic particle swarm optimization algorithm and the improved particle swarm optimization algorithm proposed in this paper. The results show that the proposed method has a stronger search ability, higher accuracy and better stability.",

keywords = "Optimization allocation, Reliability, Swarm optimization algorithm",

author = "Qihai Liang and Zhe Wang and Jinzhu Qu and Xiaojian Yi",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019 ; Conference date: 15-08-2019 Through 17-08-2019",

year = "2019",

month = aug,

doi = "10.1109/SDPC.2019.00084",

language = "English",

series = "Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "429--434",

editor = "Chuan Li and Shaohui Zhang and Jianyu Long and Diego Cabrera and Ping Ding",

booktitle = "Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019",

address = "United States",

}

Liang, Q, Wang, Z, Qu, J & Yi, X 2019, Reliability optimization allocation method based on improved dynamic particle swarm optimization. in C Li, S Zhang, J Long, D Cabrera & P Ding (eds), Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019., 9168924, Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019, Institute of Electrical and Electronics Engineers Inc., pp. 429-434, 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019, Beijing, China, 15/08/19. https://doi.org/10.1109/SDPC.2019.00084

Reliability optimization allocation method based on improved dynamic particle swarm optimization. / Liang, Qihai; Wang, Zhe; Qu, Jinzhu et al.
Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019. ed. / Chuan Li; Shaohui Zhang; Jianyu Long; Diego Cabrera; Ping Ding. Institute of Electrical and Electronics Engineers Inc., 2019. p. 429-434 9168924 (Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Reliability optimization allocation method based on improved dynamic particle swarm optimization

AU - Liang, Qihai

AU - Wang, Zhe

AU - Qu, Jinzhu

AU - Yi, Xiaojian

PY - 2019/8

Y1 - 2019/8

N2 - Aiming at the reliability optimization allocation, the basic particle swarm optimization algorithm has the disadvantages of slow convergence and easy to fall into local extremum. This paper proposes an improved dynamic particle swarm optimization algorithm, which uses the extrapolation technique in mathematics to guide the evolution direction of particles. At the same time, the idea of transforming the multimodal function and dynamically adjusting the group size is introduced, and the elite set is used to preserve the optimal individual of each generation. Finally, this paper uses a certain type of sonar as an example to establish a reliability mathematical model, which uses the basic particle swarm optimization algorithm and the improved particle swarm optimization algorithm proposed in this paper. The results show that the proposed method has a stronger search ability, higher accuracy and better stability.

AB - Aiming at the reliability optimization allocation, the basic particle swarm optimization algorithm has the disadvantages of slow convergence and easy to fall into local extremum. This paper proposes an improved dynamic particle swarm optimization algorithm, which uses the extrapolation technique in mathematics to guide the evolution direction of particles. At the same time, the idea of transforming the multimodal function and dynamically adjusting the group size is introduced, and the elite set is used to preserve the optimal individual of each generation. Finally, this paper uses a certain type of sonar as an example to establish a reliability mathematical model, which uses the basic particle swarm optimization algorithm and the improved particle swarm optimization algorithm proposed in this paper. The results show that the proposed method has a stronger search ability, higher accuracy and better stability.

KW - Optimization allocation

KW - Reliability

KW - Swarm optimization algorithm

UR - http://www.scopus.com/inward/record.url?scp=85091513903&partnerID=8YFLogxK

U2 - 10.1109/SDPC.2019.00084

DO - 10.1109/SDPC.2019.00084

M3 - Conference contribution

AN - SCOPUS:85091513903

T3 - Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019

SP - 429

EP - 434

BT - Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019

A2 - Li, Chuan

A2 - Zhang, Shaohui

A2 - Long, Jianyu

A2 - Cabrera, Diego

A2 - Ding, Ping

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019

Y2 - 15 August 2019 through 17 August 2019

ER -

Liang Q, Wang Z, Qu J, Yi X. Reliability optimization allocation method based on improved dynamic particle swarm optimization. In Li C, Zhang S, Long J, Cabrera D, Ding P, editors, Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 429-434. 9168924. (Proceedings - 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2019). doi: 10.1109/SDPC.2019.00084

Reliability optimization allocation method based on improved dynamic particle swarm optimization

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Keywords

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