A dynamic constrained multi-objective evolutionary algorithm with a dual prediction mechanism - Yang, Zhe; Deng, Libao; Li, Chunlei; Qin, Yifan; Zhang, Lili (Expert systems with applications, 2026)
•Biased sampling using predicted similarity across multiple environments.•Improved prediction method applied as an initialization and search strategy.•Strategy to reuse evaluation budget from environmental detection.•Dual-population static optimizer for dynamic constrained multi-objective problems. The design of evolutionary algorithms for dynamic constrained multi-objective optimization problems is a significant research area, driven by the need for real-time adaptive solutions in complex real-world systems. To address this, this paper proposes DP-DCMOA, an algorithm that introduces a novel hybrid dynamic response by synergistically combining memory-based sampling and prediction-based generation. The algorithm’s novelty lies in its dual prediction approach. For the memory-based component, it leverages the predictable time-series properties of environmental similarity to guide a robust biased sampling from historical archives. For the prediction-based component, it employs an enhanced trajectory predictor to generate new, high-quality solutions. Furthermore, a key innovation is the integration of this trajectory predictor as a continuous search operator within the static optimizer, moving beyond its traditional role as a one-shot re-initialization tool. This dual-response system is supported by a specialized dual-population static optimizer. The effectiveness of the framework is validated through extensive experiments, demonstrating significant performance advantages over state-of-the-art algorithms and a high ranking in academic competitions.