Journal of System Simulation ›› 2026, Vol. 38 ›› Issue (10): 2939-2963.doi: 10.16182/j.issn1004731x.joss.25-1088

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

Dynamic Multi-objective Optimization Algorithm Based on Residual-driven Two-stage Prediction and Its Application

Zhang Xin, Wang Han, Qian Pengjiang, Fang Wei   

  1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214000, China
  • Received:2025-11-06 Revised:2025-12-04 Online:2026-10-31 Published:2026-10-20

Abstract:

A dynamic multi-objective optimization algorithm based on residual-driven two-stage prediction (RDTP) is proposed to address the dilemma of prediction strategies in dynamic multi-objective optimization struggling with insufficient samples in the early stage and low accuracy in the later stage. In the initial stage, if an environmental change is detected, grey prediction, which is fast and suitable for small samples, is employed to generate individuals for the new environment. A residual analysis is performed to determine whether to switch to a LSSVM to improve prediction accuracy as more samples become available. The Pareto front is clustered to select detection objects. The population for the new environment is composed of random solutions, old elite solutions, predicted solutions, and solutions from their neighborhood, thereby increasing population diversity and accelerating algorithm convergence. Simulation results demonstrate that the proposed algorithm achieves an excellent response speed to environmental changes and can efficiently obtain greener, more time-saving, and lower-cost paths in dynamic multi-objective vehicle routing problems.

Key words: dynamic multi-objective optimization, prediction strategy, grey model, least squares support vector machine, dynamic multi-objective vehicle routing

CLC Number: