Journal of System Simulation ›› 2026, Vol. 38 ›› Issue (10): 2887-2904.doi: 10.16182/j.issn1004731x.joss.26-0401

• Intelligent Optimization Algorithms and Autonomous Decision?Making • Previous Articles     Next Articles

Manifold Alignment and Dual Prediction-based Adaptive Dynamic Multi-objective Evolutionary Algorithm

Yue Xuezhi1, Xiong Chao1, Jiang Yanshen1, Guo Zhaolu1, Peng Hu2   

  1. 1.School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China
    2.School of Computer and Big Data Science, Jiujiang University, Jiujiang 332005, China
  • Received:2026-04-30 Revised:2026-05-22 Online:2026-10-31 Published:2026-10-20

Abstract:

To address prediction failure, manifold topology distortion, and insufficient adaptability in environmental response and operator configuration in existing prediction-based algorithms for dynamic multi-objective optimization under complex nonlinear environments and small-sample settings, this paper proposes a manifold alignment and dual prediction-based adaptive dynamic multi-objective evolutionary algorithm, termed MADP-DMOEA. When the environment changes, the algorithm decomposes the decision-variable manifold into center and shape components, effectively aligns manifolds at adjacent time instants via bipartite optimal matching, and identifies environmental change severity using geometric displacement. A dual prediction model is developed, in which a GRNN performs center extrapolation under severe changes, while an echo state network (ESN) estimates epistemic uncertainty to adaptively adjust the perturbation scale under mild changes. Operator scheduling based on dynamic convergence feedback and environmental selection that preserves decision-space topology are introduced. Experimental results demonstrate that the proposed algorithm achieves good convergence accuracy, diversity of solution distribution, and dynamic tracking performance in complex time-varying environments and effectively improves the tracking of the Pareto-optimal solution set.

Key words: dynamic multi-objective optimization, manifold alignment, adaptive strategy, generalized regression neural network, bipartite graph matching

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