Abstract
In recent years, researchers have made significant progress in handling dynamic multi-objective optimization problems (DMOPs), particularly for environmental changes with predictable characteristics. However, little attention has been paid to DMOPs with stochastic changes. It may be difficult for existing dynamic multi-objective evolutionary algorithms (DMOEAs) to effectively handle this kind of DMOPs because most DMOEAs assume that environmental changes follow regular patterns and consecutive environments are similar. This paper presents a Mahalanobis Distance-based approach (MDA) to deal with DMOPs with stochastic changes. Specifically, we make an all-sided assessment of search environments via Mahalanobis distance on saved information to learn the relationship between the new environment and historical ones. Afterward, a change response strategy applies the learning to the new environment to accelerate the convergence and maintain the diversity of the population. Besides, the change degree is considered for all decision variables to alleviate the impact of stochastic changes on the evolving population. MDA has been tested on stochastic DMOPs with 2 to 4 objectives. The results show that MDA performs significantly better than the other latest algorithms in this paper, suggesting that MDA is effective for DMOPs with stochastic changes.
Original language | English |
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Pages (from-to) | 238-251 |
Number of pages | 14 |
Journal | IEEE Transactions on Evolutionary Computation |
Volume | 28 |
Issue number | 1 |
Early online date | 8 Mar 2023 |
DOIs | |
Publication status | Published - 1 Feb 2024 |
Bibliographical note
Funding information:This work was supported in part by the National Natural Science Foundation of China under Grant 62176228 and Grant 62276224; in part by the National Science Fund for Outstanding Young Scholars under Grant 62122093; in part by the Natural Science Foundation of Hunan Province under Grant 2020JJ4590; and in part by the Education Department Project of Hunan Province under Grant 21A0444 and Grant 22B0139.
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/TEVC.2023.3253850.Keywords
- algorithms
- dynamic multiobjective optimization
- Mahalanobis distance (MD)
- stochastic changes