A hybird method based on particle swarm optimization and moth-flame optimization

Zhenlun Yang; Kunquan Shi; Angus Wu; Meiling Qiu; Yaomin Hu

doi:10.1109/IHMSC.2019.10144

A hybird method based on particle swarm optimization and moth-flame optimization

Zhenlun Yang
, Kunquan Shi
, Angus Wu
, Meiling Qiu
, Yaomin Hu

Department of Electrical Engineering

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

14 Citations (Scopus)

Abstract

This paper presents a novel hybrid optimization algorithm based on particle swarm optimization (PSO) and moth-flame optimization (MFO), called Hy-PSO-MFO, for solving the function optimization problems. In this algorithm, the ideas of PSO and MFO are integrated in a rational way to overcome the drawbacks of PSO and MFO, and thus improve the global search ability. The performance of the proposed algorithm is investigated on solving the well-known benchmark functions. The experimental results demonstrate that the HyPSO-MFO performs better than the standard PSO and original MFO in terms of solution quality and convergence speed.

Original language	English
Title of host publication	2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics IHMSC 2019
Subtitle of host publication	Proceedings
Publisher	IEEE
Pages	207-210
Volume	2
ISBN (Electronic)	9781728118598
ISBN (Print)	9781728118604
DOIs	https://doi.org/10.1109/IHMSC.2019.10144
Publication status	Published - 2019
Event	2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2019 - Hangzhou, China Duration: 24 Aug 2019 → 25 Aug 2019 http://ihmsc.zju.edu.cn/

Publication series

Name	Proceedings - International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC

Conference

Conference	2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2019
Abbreviated title	IHMSC 2019
Place	China
City	Hangzhou
Period	24/08/19 → 25/08/19
Internet address	http://ihmsc.zju.edu.cn/

Research Keywords

particle swarm optimization
moth-flame optimization
hybrid method

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10.1109/IHMSC.2019.10144

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Yang, Z., Shi, K., Wu, A., Qiu, M., & Hu, Y. (2019). A hybird method based on particle swarm optimization and moth-flame optimization. In 2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics IHMSC 2019: Proceedings (Vol. 2, pp. 207-210). Article 8941442 (Proceedings - International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC). IEEE. https://doi.org/10.1109/IHMSC.2019.10144

Yang, Zhenlun ; Shi, Kunquan ; Wu, Angus et al. / A hybird method based on particle swarm optimization and moth-flame optimization. 2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics IHMSC 2019: Proceedings. Vol. 2 IEEE, 2019. pp. 207-210 (Proceedings - International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC).

@inproceedings{f431382d7fec4149b062492cf0e05a10,

title = "A hybird method based on particle swarm optimization and moth-flame optimization",

abstract = "This paper presents a novel hybrid optimization algorithm based on particle swarm optimization (PSO) and moth-flame optimization (MFO), called Hy-PSO-MFO, for solving the function optimization problems. In this algorithm, the ideas of PSO and MFO are integrated in a rational way to overcome the drawbacks of PSO and MFO, and thus improve the global search ability. The performance of the proposed algorithm is investigated on solving the well-known benchmark functions. The experimental results demonstrate that the HyPSO-MFO performs better than the standard PSO and original MFO in terms of solution quality and convergence speed.",

keywords = "particle swarm optimization, moth-flame optimization, hybrid method",

author = "Zhenlun Yang and Kunquan Shi and Angus Wu and Meiling Qiu and Yaomin Hu",

year = "2019",

doi = "10.1109/IHMSC.2019.10144",

language = "English",

isbn = "9781728118604",

volume = "2",

series = "Proceedings - International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC",

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booktitle = "2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics IHMSC 2019",

address = "United States",

note = "2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2019, IHMSC 2019 ; Conference date: 24-08-2019 Through 25-08-2019",

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Yang, Z, Shi, K, Wu, A, Qiu, M & Hu, Y 2019, A hybird method based on particle swarm optimization and moth-flame optimization. in 2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics IHMSC 2019: Proceedings. vol. 2, 8941442, Proceedings - International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC, IEEE, pp. 207-210, 2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2019, Hangzhou, China, 24/08/19. https://doi.org/10.1109/IHMSC.2019.10144

A hybird method based on particle swarm optimization and moth-flame optimization. / Yang, Zhenlun; Shi, Kunquan; Wu, Angus et al.
2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics IHMSC 2019: Proceedings. Vol. 2 IEEE, 2019. p. 207-210 8941442 (Proceedings - International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A hybird method based on particle swarm optimization and moth-flame optimization

AU - Yang, Zhenlun

AU - Shi, Kunquan

AU - Wu, Angus

AU - Qiu, Meiling

AU - Hu, Yaomin

PY - 2019

Y1 - 2019

N2 - This paper presents a novel hybrid optimization algorithm based on particle swarm optimization (PSO) and moth-flame optimization (MFO), called Hy-PSO-MFO, for solving the function optimization problems. In this algorithm, the ideas of PSO and MFO are integrated in a rational way to overcome the drawbacks of PSO and MFO, and thus improve the global search ability. The performance of the proposed algorithm is investigated on solving the well-known benchmark functions. The experimental results demonstrate that the HyPSO-MFO performs better than the standard PSO and original MFO in terms of solution quality and convergence speed.

AB - This paper presents a novel hybrid optimization algorithm based on particle swarm optimization (PSO) and moth-flame optimization (MFO), called Hy-PSO-MFO, for solving the function optimization problems. In this algorithm, the ideas of PSO and MFO are integrated in a rational way to overcome the drawbacks of PSO and MFO, and thus improve the global search ability. The performance of the proposed algorithm is investigated on solving the well-known benchmark functions. The experimental results demonstrate that the HyPSO-MFO performs better than the standard PSO and original MFO in terms of solution quality and convergence speed.

KW - particle swarm optimization

KW - moth-flame optimization

KW - hybrid method

UR - https://www.scopus.com/pages/publications/85078302681

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85078302681&origin=recordpage

U2 - 10.1109/IHMSC.2019.10144

DO - 10.1109/IHMSC.2019.10144

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781728118604

VL - 2

T3 - Proceedings - International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC

SP - 207

EP - 210

BT - 2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics IHMSC 2019

PB - IEEE

T2 - 2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2019

Y2 - 24 August 2019 through 25 August 2019

ER -

Yang Z, Shi K, Wu A, Qiu M, Hu Y. A hybird method based on particle swarm optimization and moth-flame optimization. In 2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics IHMSC 2019: Proceedings. Vol. 2. IEEE. 2019. p. 207-210. 8941442. (Proceedings - International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC). Epub 2019 Dec 26. doi: 10.1109/IHMSC.2019.10144

A hybird method based on particle swarm optimization and moth-flame optimization

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