一种基于多智能体协同优化的异构资源融合调度方法
By employing a multi-agent collaborative and bidirectional ant colony optimization algorithm, a heterogeneous resource graph and task requirement graph are constructed, candidate scheduling schemes are generated, and a pheromone ledger is used for reversible updates and phase adaptive adjustment. This solves the problems of premature convergence and load imbalance in heterogeneous resource scheduling, and achieves efficient and interpretable resource allocation and scheduling optimization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- WUHU BIG DATA CONSTRUCTION INVESTMENT & OPERATION CO LTD
- Filing Date
- 2025-12-19
- Publication Date
- 2026-07-17
AI Technical Summary
Existing heterogeneous resource scheduling methods suffer from problems such as premature pheromone updates, unbalanced load, insufficient interpretability, and difficulty in forming a traceable closed loop in multi-agent scenarios, leading to resource allocation conflicts and low efficiency.
A multi-agent collaborative and bidirectional staking ant colony optimization algorithm is adopted. By constructing a heterogeneous resource graph and task requirement graph, candidate scheduling schemes are generated. The pheromone ledger is used for reversible updates. Combined with phase adaptive adjustment and overflow tax return mechanism, resource load balancing and scheduling optimization are achieved.
It achieves robustness, load balancing, and result interpretability in heterogeneous resource scheduling, improves resource allocation accuracy and scheduling decision transparency, and significantly enhances system stability and global convergence performance.
Smart Images
Figure CN121807535B_ABST