A Discriminative Prediction Strategy Based on Multi-View Knowledge Transfer for Dynamic Multi-Objective Optimization

Dynamic multi-objective optimization problems (DMOPs) are widely encountered in engineering optimization processes, characterized by conflicting objectives that change over time. Evolutionary transfer optimization (ETO) has recently emerged as a promising optimization paradigm for addressing DMOPs. ETO-based dynamic multi-objective evolutionary algorithms (DMOEAs) usually adopt discriminative prediction strategies to select high-quality initial solutions in new environments by transferring knowledge from the source domain, thereby accelerating the evolutionary process. However, DMOPs often change in both decision and objective spaces. Existing methods rely on a single view from the source domain for knowledge transfer, which can overlook crucial knowledge from other views, potentially affecting the selection of high-quality initial solutions and limiting optimization performance. To address this, we propose a discriminative prediction strategy based on multi-view knowledge transfer for DMOEA, called MKT-DMOEA. Specifically, we construct discriminative predictors from the view of the decision space and objective space in the source domain. Each discriminative predictor effectively reduces the differences between environments under the current view through a transfer strategy based on geometric feature transformation. Meanwhile, these predictors make full use of the transferred knowledge to achieve accurate discriminative predictions under the current view. Finally, we integrate the discriminative prediction results from multiple views to select high-quality initial solutions. Experimental results demonstrate that our proposed algorithm outperforms four other state-of-the-art DMOEAs in terms of both diversity and convergence on the well-known CEC 2018 dynamic multi-objective optimization benchmark suite DF.