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A Mahalanobis Distance-Based Approach for Dynamic Multiobjective Optimization With Stochastic Changes
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DOI：10.1109/TEVC.2023.3253850.png)
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In recent years, researchers have made significant progress in handling dynamic multiobjective 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 multiobjective 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 article 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. An MDA has been tested on stochastic DMOPs with two to four objectives. The results show that MDA performs significantly better than the other latest algorithms in this article, suggesting that MDA is effective for DMOPs with stochastic changes.
Keyword:
Optimization
Statistics
Sociology
Convergence
Stochastic processes
Maintenance engineering
Dynamic scheduling
Algorithms
dynamic multiobjective optimization
Mahalanobis distance (MD)
stochastic changes
期刊
IF:
12
论文数:
1.9K
被引数:
2.4W
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引用论文
Evolutionary Dynamic Multiobjective Optimization: Benchmarks and Algorithm Comparisons进化动态多目标优化: 基准和算法比较
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分享收藏High-level Fusion Coupled with Mahalanobis Distance Weighted (MDW) Method for Multivariate Calibration.png)
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