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A hybrid prediction based dynamic multiobjective optimization algorithm via a linear strategy and diffusion model
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DOI：10.1007/s10586-026-06340-1.png)
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Abstract
En 中文
In dynamic multiobjective optimization problems (DMOPs), quickly tracking the Pareto fronts under various changing patterns has been a key challenge. Prediction driven methods have shown strong potential in capturing these changing patterns; however, most existing approaches rely on linear models, which limits their effectiveness in addressing nonlinear changes. To address this issue, we propose a novel dynamic multiobjective evolutionary algorithm named LDFP-DMOEA that combines a linear strategy with a diffusion model. Specifically, the population is adaptively divided into two subpopulations according to the correlation of individual variations across consecutive environments. A linear prediction strategy is applied to the highly correlated subpopulation to exploit stable trends, while a diffusion model is employed for the less correlated subpopulation to capture complex nonlinear dynamics. The two subpopulations are then merged, followed by a local adjustment mechanism to enhance diversity. Experimental results show that LDFP outperforms five representative comparative algorithms on multiple benchmark problems, validating its effectiveness and superiority.
Keywords:
Dynamic multiobjective optimization
Evolutionary algorithm
Prediction
Diffusion model
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4.1
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5.1K
Citations:
7.5K
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