Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
A new branch-and-cut algorithm for the capacitated vehicle routing problem
Mathematical Programming: Series A and B
Cellular Ants: Combining Ant-Based Clustering with Cellular Automata
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Robust Branch-and-Cut-and-Price for the Capacitated Vehicle Routing Problem
Mathematical Programming: Series A and B
An efficient variable neighborhood search heuristic for very large scale vehicle routing problems
Computers and Operations Research
Active-guided evolution strategies for large-scale capacitated vehicle routing problems
Computers and Operations Research
Computers and Industrial Engineering
An improved heuristic for the capacitated arc routing problem
Computers and Operations Research
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
Dynamic learning rate optimization of the backpropagation algorithm
IEEE Transactions on Neural Networks
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This paper presents a multi-cellular-ant algorithm for large scale capacitated vehicle routing problem with restrictive vehicle capability. The problem is divided into corresponding smaller ones by a decomposition methodology. Relative relationship between subsystems will be solved through cooperative performance among cellular ants to avoid trivial solutions. The empirical results composed with adaptive ant colony algorithm and traditional collaboration show more efficiency and availability.