Research on VRP using advanced probability learning based evolutionary algorithm

Authors:
Shanshan Wan;Ying Hao;Dongwei Qiu
Affiliations:
Beijing University of Civil Engineering and Architecture, Beijing, China;Beijing University of Civil Engineering and Architecture, Beijing, China;Beijing University of Civil Engineering and Architecture, Beijing, China
Venue:
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Year:
2009

Citing 3
Cited 0

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Ant system: optimization by a colony of cooperating agents

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Abstract

Vehicle routing optimization problem with time constraint is researched in this paper and a hybrid optimization algorithm-PBIL combined with ant algorithm is proposed and applied to VRP. The objective function is to minimize the cost and reduce the loss caused by customers' time restriction. The probability matrix of PBIL algorithm is modified with the positive feedback and information disappearing mechanisms of ant algorithm. Also, the probability statistics for the road traffic status distributed is considered in decision-making for the vehicles' routing. The hybrid intelligent evolutionary algorithm is tested on Jingyuanda Logistics Company and the good validity, accuracy, and stability performance are fully proved.