Evolutionary Dynamic Multi-Objective Optimization With Learning Across Problems

Dynamic multi-objective optimization problems (DMOPs) are prevalent in many practical applications and have garnered significant attention from both industry and academia, leading to the proposal of numerous dynamic multi-objective optimization algorithms. Among these approaches, learning-and prediction-based evolutionary approaches have achieved remarkable success due to their fast learning and strong optimization capabilities when dynamic occurs. However, existing methods typically focus on learning and transferring knowledge within a single problem, often relying on historical knowledge to aid the optimization process. This could limit the data available for developing a more general prediction model for dynamic optimization. The potential for leveraging knowledge across different DMOPs to enhance problem-solving efficiency and effectiveness remains largely unexplored. Building on this insight, this paper explores the solution of DMOPs with learning not only within a single problem but also across different problems. In particular, by performing dynamic feature extraction and task-specific solution classification, we propose to construct a centralized learning model that captures the correlations across DMOPs and the corresponding optimized solutions. This approach allows optimization data from multiple problems to be effectively utilized, enabling the learning of dynamic knowledge to enhance evolutionary optimization when a change occurs. To assess the effectiveness of the proposed algorithm, extensive empirical studies have been conducted on the commonly used DMOP benchmarks with fixed and shifty dynamic settings. The numerical results demonstrate the effectiveness of the proposed algorithm in recognizing more general change patterns of DMOPs, showcasing its superiority compared to the existing state-of-the-art learning and prediction-based approaches.