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

Authors

• Xin Zhang, School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214000, China
• Han Wang, School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214000, China
• Pengjiang Qian, School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214000, China
• Wei Fang, School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214000, China

Abstract

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.

Recommended Citation

Zhang, Xin; Wang, Han; Qian, Pengjiang; and Fang, Wei (2026) "Dynamic Multi-objective Optimization Algorithm Based on Residual-driven Two-stage Prediction and Its Application," Journal of System Simulation: Vol. 38: Iss. 10, Article 11.
DOI: 10.16182/j.issn1004731x.joss.25-1088
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss10/11

First Page

2939

Last Page

2963

CLC

TP273

Recommended Citation

Zhang Xin, Wang Han, Qian Pengjiang, et al. Dynamic Multi-objective Optimization Algorithm Based on Residual-driven Two-stage Prediction and Its Application[J]. Journal of System Simulation, 2026, 38(10): 2939-2963.

DOI

10.16182/j.issn1004731x.joss.25-1088