Multi-objective Flexible Job Shop Scheduling Problem with transportation constraint solved with NSGA-II, VNS and improved initialisation
- Updated
Aug 7, 2024 - Python
Multi-objective Flexible Job Shop Scheduling Problem with transportation constraint solved with NSGA-II, VNS and improved initialisation
Source code for the paper "Energy-Efficient Client Sampling for Federated Learning in Heterogeneous Mobile Edge Computing Networks", this paper is pulished in ICC 2024.
Adaptive Sparse Training (AST): 92.1% ImageNet-100 accuracy with 61% energy savings and zero degradation. Production-ready implementations for energy-efficient deep learning with ResNet50 and modern architectures.
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