A dynamic multi-objective evolutionary algorithm based on Mahalanobis distance and intra-cluster individual correlation rectification

Responding quickly and accurately to environmental changes is a challenge in addressing dynamic multi-objective optimization problems (DMOPs). Although many dynamic multi-objective evolutionary algorithms (DMOEAs) have demonstrated impressive performance, there is still room for improvement in accur...

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Bibliographic Details
Published in:Information sciences Vol. 678; p. 120922
Main Authors: Ge, Fangzhen, Hou, Xing, Chen, Debao, Shen, Longfeng, Liu, Huaiyu
Format: Journal Article
Language:English
Published: Elsevier Inc 01.09.2024
Subjects:
Correlation analysis
Dynamic multi-objective optimization
Evolutionary algorithms
Mahalanobis distance
Prediction
Time series
Evolutionary algorithms
Dynamic multi-objective optimization
Correlation analysis
Mahalanobis distance
Prediction
Time series
ISSN:0020-0255, 1872-6291
Online Access:Get full text
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Summary:Responding quickly and accurately to environmental changes is a challenge in addressing dynamic multi-objective optimization problems (DMOPs). Although many dynamic multi-objective evolutionary algorithms (DMOEAs) have demonstrated impressive performance, there is still room for improvement in accurately predicting population behavior. To address this limitation, this paper proposes a prediction strategy based on Mahalanobis distance and intra-cluster individual correlation rectification (MCIR) to deal with DMOPs. First, a manifold clustering method is used to partition the Pareto set of the population into subpopulations, in which individuals with similar movement trends are grouped into clusters. Second, the Mahalanobis distance is introduced to systematically measure the relationships between clusters in adjacent environments. Time series models are established for each cluster center to predict their positions in the neighboring environment. On this basis, the movement characteristics of individuals within clusters are further rectified by calculating the correlation between intra-cluster individuals and the cluster center, facilitating more accurate tracking of the changing Pareto set/Pareto front. Finally, Gaussian noise is introduced to ensure the diversity of new individuals. The effectiveness of the MCIR algorithm is demonstrated by comparing it with four DMOEAs using 18 test instances. Experimental results confirm that MCIR holds great promise in addressing DMOPs. •An effective strategy based on Mahalanobis distance is proposed for constructing time series in dynamic multi-objective optimization problems.•An incremental rectification strategy using correlation analysis is introduced to estimate individual movement trends accurately.•Comparative results on DMOP test suites show the algorithm has a distinct competitive advantage and potential over others.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2024.120922