Handling Dynamic Multiobjective Optimization Environments via Layered Prediction and Subspace-Based Diversity Maintenance

Yaru Hu; Jinhua Zheng; Shouyong Jiang; Shengxiang Yang; Juan Zou

doi:10.1109/TCYB.2021.3128584

Handling Dynamic Multiobjective Optimization Environments via Layered Prediction and Subspace-Based Diversity Maintenance

Yaru Hu, Jinhua Zheng^* (Corresponding Author), Shouyong Jiang^* (Corresponding Author), Shengxiang Yang, Juan Zou

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

26 Citations (Scopus)

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Abstract

In this article, we propose an evolutionary algorithm based on layered prediction (LP) and subspace-based diversity maintenance (SDM) for handling dynamic multiobjective optimization (DMO) environments. The LP strategy takes into account different levels of progress by different individuals in evolution and historical information to predict the population in the event of environmental changes for a prompt change response. The SDM strategy identifies gaps in population distribution and employs a gap-filling technique to increase population diversity. SDM further guides rational population reproduction with a subspace-based probability model to maintain the balance between population diversity and convergence in every generation of evolution regardless of environmental changes. The proposed algorithm has been extensively studied through comparison with five state-of-the-art algorithms on a variety of test problems, demonstrating its effectiveness in dealing with DMO problems.

Original language	English
Pages (from-to)	2572 - 2585
Number of pages	14
Journal	IEEE Transactions on Cybernetics
Volume	53
Issue number	4
Early online date	15 Dec 2021
DOIs	https://doi.org/10.1109/TCYB.2021.3128584
Publication status	Published - 1 Apr 2023

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under Grant 62176228 and Grant 61876164; in part by the Natural Science Foundation of Hunan Province under Grant 2020JJ4590; and in part by the Education Department Major Project of Hunan Province under Grant 17A212.

Data Availability Statement

This article has supplementary material provided by the authors and color versions of one or more figures available at https://doi.org/10.1109/TCYB.2021.3128584

Keywords

Change response
Convergence
dynamic multiobjective optimization (DMO)
gap filling
Heuristic algorithms
layered prediction (LP)
Linear programming
Maintenance engineering
Optimization
Sociology
Statistics
subspace-based diversity maintenance (SDM)

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10.1109/TCYB.2021.3128584Licence: Unspecified

Hu_etal_IEEETC_Handling_Dynamic_Multi_AAM
This is the accepted version of "Y. Hu, J. Zheng, S. Jiang, S. Yang and J. Zou, "Handling Dynamic Multiobjective Optimization Environments via Layered Prediction and Subspace-Based Diversity Maintenance," in IEEE Transactions on Cybernetics, doi: 10.1109/TCYB.2021.3128584" © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Accepted author manuscript, 5.47 MBLicence: Unspecified

Cite this

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title = "Handling Dynamic Multiobjective Optimization Environments via Layered Prediction and Subspace-Based Diversity Maintenance",

abstract = "In this article, we propose an evolutionary algorithm based on layered prediction (LP) and subspace-based diversity maintenance (SDM) for handling dynamic multiobjective optimization (DMO) environments. The LP strategy takes into account different levels of progress by different individuals in evolution and historical information to predict the population in the event of environmental changes for a prompt change response. The SDM strategy identifies gaps in population distribution and employs a gap-filling technique to increase population diversity. SDM further guides rational population reproduction with a subspace-based probability model to maintain the balance between population diversity and convergence in every generation of evolution regardless of environmental changes. The proposed algorithm has been extensively studied through comparison with five state-of-the-art algorithms on a variety of test problems, demonstrating its effectiveness in dealing with DMO problems.",

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note = " This work was supported in part by the National Natural Science Foundation of China under Grant 62176228 and Grant 61876164; in part by the Natural Science Foundation of Hunan Province under Grant 2020JJ4590; and in part by the Education Department Major Project of Hunan Province under Grant 17A212.",

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N2 - In this article, we propose an evolutionary algorithm based on layered prediction (LP) and subspace-based diversity maintenance (SDM) for handling dynamic multiobjective optimization (DMO) environments. The LP strategy takes into account different levels of progress by different individuals in evolution and historical information to predict the population in the event of environmental changes for a prompt change response. The SDM strategy identifies gaps in population distribution and employs a gap-filling technique to increase population diversity. SDM further guides rational population reproduction with a subspace-based probability model to maintain the balance between population diversity and convergence in every generation of evolution regardless of environmental changes. The proposed algorithm has been extensively studied through comparison with five state-of-the-art algorithms on a variety of test problems, demonstrating its effectiveness in dealing with DMO problems.

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Handling Dynamic Multiobjective Optimization Environments via Layered Prediction and Subspace-Based Diversity Maintenance

Abstract

Bibliographical note

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Keywords

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