Table 5 Actual application effect of MSC-PSO-IICA model.
Parameter category | Parameter name | Value | Application scenario |
|---|---|---|---|
Multi-objective optimization | Pareto front coverage (%) | 96.78 | Multi-objective balance (cost & carbon emissions) optimization |
Path planning | Dynamic path adjustment success rate (%) | 98.24 | Real-time obstacle avoidance in typhoon scenarios (GIS + LSTM integration) |
Inventory management | Inventory turnover rate improvement (%) | 32.15 | Smart replenishment strategy for B/C-class supplies |
Energy consumption optimization | Transportation energy reduction (%) | 22.73 | Multi-modal energy synergy optimization (truck-drone collaboration) |
Computational efficiency | Single iteration time (seconds) | 0.45 | Solving efficiency for logistics optimization problems |
Solution quality | Global optimum deviation (Std. Dev.) | 0.08 | Solution stability in complex multimodal scenarios |
Emergency response | Material mismatch rate (%) | 1.23 | Precise matching of D-class emergency supplies (with RFID verification) |
Algorithm robustness | High-dimensional convergence success rate (%) | 89.12 | Global convergence in 500-dimensional node optimization |
