MOEA/D With Spatial–Temporal Topological Tensor Prediction for Evolutionary Dynamic Multiobjective Optimization
针对动态多目标优化中预测初始种群精度低的问题，提出一种基于时空拓扑张量的预测方法，利用环境间种群分布的拓扑相似性，通过改进的张量多短时间序列预测生成新环境初始解，在基准和实际问题上表现优于现有算法。
When solving dynamic multiobjective optimization problems, most evolutionary algorithms attempt to predict the initial population in a new environment by mining the relationships between solutions during historical environment changes. However, the complex relationships between solutions and the limited amount of available data often make it difficult to extract useful information efficiently, which may deteriorate the prediction accuracy. To address this problem, this paper proposes a spatial-temporal topological tensor-based prediction method to generate the initial population in a new environment under the decomposition framework of MOEA/D. The method relies on the idea that the population distribution in each environment has topological similarity along the time dimension in the objective space, which makes it efficient to represent the population distribution in terms of a tensor and predict new solutions along each decomposition axis in a new environment by an improved tensor-based multi-short time series prediction method. Experimental results on various benchmark problems and a real-world problem show that the proposed method is competitive or even superior to state-of-the-art dynamic multiobjective evolutionary algorithms based on prediction strategies.