An Evolutionary Dynamic Multi-objective Optimization Algorithm Based on Center-point Prediction and Sub-population Autonomous Guidance

Jianwei ZHOU; Juan ZOU; Shengxiang YANG; Gan RUAN; Junwei OU; Jinhua ZHENG

doi:10.1109/SSCI.2018.8628655

An Evolutionary Dynamic Multi-objective Optimization Algorithm Based on Center-point Prediction and Sub-population Autonomous Guidance

Jianwei ZHOU, Juan ZOU^*, Shengxiang YANG, Gan RUAN, Junwei OU, Jinhua ZHENG

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

7 Citations (Scopus)

Abstract

Dynamic multi-objective optimization problems (DMOPs) provide a challenge in that objectives conflict each other and change over time. In this paper, a hybrid approach based on prediction and autonomous guidance is proposed, which responds the environmental changes by generating a new population. According to the position of historical population, a part of the population is generated by predicting roughly and quickly. In addition, another part of the population is generated by autonomous guidance. A sub-population from current population evolves several generations independently, which guides the current population into the promising area. Compared with other three algorithms on a series of benchmark problems, the proposed algorithm is competitive in convergence and diversity. Empirical results indicate its superiority in dealing with dynamic environments.

Original language	English
Title of host publication	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018
Editors	Suresh SUNDARAM
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2148-2154
Number of pages	7
ISBN (Electronic)	9781538692769
DOIs	https://doi.org/10.1109/SSCI.2018.8628655
Publication status	Published - 2 Jul 2018
Externally published	Yes

Publication series

Name	Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018

Bibliographical note

This work was supported by the research projects: the National Natural Science Foundation of China under Grant Nos. 61502408, 61673331, 61379062 and 61403326, the Education Department Major Project of Hunan Province under Grant No. 17A212, the CERNET Innovation Project under Grant No. NGII20150302, the Natural Science Foundation of Hunan Province under Grant No. 14JJ2072, the Science and Technology Plan Project of Hunan Province under Grant No. 2016TP1020, the Provinces and Cities Joint Foundation Project under Grant No. 2017JJ4001.

Publisher Copyright:
© 2018 IEEE.

Keywords

Autonomous guidance
dynamic multi-objective optimization

Access to Document

10.1109/SSCI.2018.8628655

Cite this

ZHOU, J., ZOU, J., YANG, S., RUAN, G., OU, J., & ZHENG, J. (2018). An Evolutionary Dynamic Multi-objective Optimization Algorithm Based on Center-point Prediction and Sub-population Autonomous Guidance. In S. SUNDARAM (Ed.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018 (pp. 2148-2154). Article 8628655 (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2018.8628655

ZHOU, Jianwei ; ZOU, Juan ; YANG, Shengxiang et al. / An Evolutionary Dynamic Multi-objective Optimization Algorithm Based on Center-point Prediction and Sub-population Autonomous Guidance. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. editor / Suresh SUNDARAM. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 2148-2154 (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018).

@inproceedings{d770dfc0e73947648778456a31eef636,

title = "An Evolutionary Dynamic Multi-objective Optimization Algorithm Based on Center-point Prediction and Sub-population Autonomous Guidance",

abstract = "Dynamic multi-objective optimization problems (DMOPs) provide a challenge in that objectives conflict each other and change over time. In this paper, a hybrid approach based on prediction and autonomous guidance is proposed, which responds the environmental changes by generating a new population. According to the position of historical population, a part of the population is generated by predicting roughly and quickly. In addition, another part of the population is generated by autonomous guidance. A sub-population from current population evolves several generations independently, which guides the current population into the promising area. Compared with other three algorithms on a series of benchmark problems, the proposed algorithm is competitive in convergence and diversity. Empirical results indicate its superiority in dealing with dynamic environments.",

keywords = "Autonomous guidance, dynamic multi-objective optimization",

author = "Jianwei ZHOU and Juan ZOU and Shengxiang YANG and Gan RUAN and Junwei OU and Jinhua ZHENG",

note = "This work was supported by the research projects: the National Natural Science Foundation of China under Grant Nos. 61502408, 61673331, 61379062 and 61403326, the Education Department Major Project of Hunan Province under Grant No. 17A212, the CERNET Innovation Project under Grant No. NGII20150302, the Natural Science Foundation of Hunan Province under Grant No. 14JJ2072, the Science and Technology Plan Project of Hunan Province under Grant No. 2016TP1020, the Provinces and Cities Joint Foundation Project under Grant No. 2017JJ4001. Publisher Copyright: {\textcopyright} 2018 IEEE.",

year = "2018",

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doi = "10.1109/SSCI.2018.8628655",

language = "English",

series = "Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018",

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ZHOU, J, ZOU, J, YANG, S, RUAN, G, OU, J & ZHENG, J 2018, An Evolutionary Dynamic Multi-objective Optimization Algorithm Based on Center-point Prediction and Sub-population Autonomous Guidance. in S SUNDARAM (ed.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018., 8628655, Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018, Institute of Electrical and Electronics Engineers Inc., pp. 2148-2154. https://doi.org/10.1109/SSCI.2018.8628655

An Evolutionary Dynamic Multi-objective Optimization Algorithm Based on Center-point Prediction and Sub-population Autonomous Guidance. / ZHOU, Jianwei; ZOU, Juan; YANG, Shengxiang et al.
Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. ed. / Suresh SUNDARAM. Institute of Electrical and Electronics Engineers Inc., 2018. p. 2148-2154 8628655 (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Research › peer-review

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AU - ZOU, Juan

AU - YANG, Shengxiang

AU - RUAN, Gan

AU - OU, Junwei

AU - ZHENG, Jinhua

N1 - This work was supported by the research projects: the National Natural Science Foundation of China under Grant Nos. 61502408, 61673331, 61379062 and 61403326, the Education Department Major Project of Hunan Province under Grant No. 17A212, the CERNET Innovation Project under Grant No. NGII20150302, the Natural Science Foundation of Hunan Province under Grant No. 14JJ2072, the Science and Technology Plan Project of Hunan Province under Grant No. 2016TP1020, the Provinces and Cities Joint Foundation Project under Grant No. 2017JJ4001. Publisher Copyright: © 2018 IEEE.

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N2 - Dynamic multi-objective optimization problems (DMOPs) provide a challenge in that objectives conflict each other and change over time. In this paper, a hybrid approach based on prediction and autonomous guidance is proposed, which responds the environmental changes by generating a new population. According to the position of historical population, a part of the population is generated by predicting roughly and quickly. In addition, another part of the population is generated by autonomous guidance. A sub-population from current population evolves several generations independently, which guides the current population into the promising area. Compared with other three algorithms on a series of benchmark problems, the proposed algorithm is competitive in convergence and diversity. Empirical results indicate its superiority in dealing with dynamic environments.

AB - Dynamic multi-objective optimization problems (DMOPs) provide a challenge in that objectives conflict each other and change over time. In this paper, a hybrid approach based on prediction and autonomous guidance is proposed, which responds the environmental changes by generating a new population. According to the position of historical population, a part of the population is generated by predicting roughly and quickly. In addition, another part of the population is generated by autonomous guidance. A sub-population from current population evolves several generations independently, which guides the current population into the promising area. Compared with other three algorithms on a series of benchmark problems, the proposed algorithm is competitive in convergence and diversity. Empirical results indicate its superiority in dealing with dynamic environments.

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ZHOU J, ZOU J, YANG S, RUAN G, OU J, ZHENG J. An Evolutionary Dynamic Multi-objective Optimization Algorithm Based on Center-point Prediction and Sub-population Autonomous Guidance. In SUNDARAM S, editor, Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 2148-2154. 8628655. (Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018). doi: 10.1109/SSCI.2018.8628655

An Evolutionary Dynamic Multi-objective Optimization Algorithm Based on Center-point Prediction and Sub-population Autonomous Guidance

Abstract

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