Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30820
Title: An Adaptive Evolutionary Algorithm for Bi- Level Multi-objective VRPs with Real-Time Traffic Conditions
Authors: Chen, Baojian 
Li, Changhe 
Zeng, Sanyou 
Yang, Shengxiang 
Mavrovouniotis, Michalis 
Major Field of Science: Natural Sciences
Field Category: Computer and Information Sciences
Keywords: Bi-Ievel routing problem;Constrained optimization;Local search;Multi-objective optimization
Issue Date: 5-Dec-2021
Source: 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021, Orlando, Florida, 5 - 7 December 2021
Conference: 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings 
Abstract: The research of vehicle routing problem (VRP) is significant for people traveling and logistics distribution. Recently, in order to alleviate global warming, the VRP based on electric vehicles has attracted much attention from researchers. In this paper, a bi-level routing problem model based on electric vehicles is presented, which can simulate the actual logistics distribution process. The classic backpropagation neural network is used to predict the road conditions for applying the method in real life. We also propose a local search algorithm based on a dynamic constrained multiobjective optimization framework. In this algorithm, 26 local search operators are designed and selected adaptively to optimize initial solutions. We also make a comparison between our algorithm and 3 modified algorithms. Experimental results indicate that our algorithm can attain an excellent solution that can satisfy the constraints of the VRP with real-time traffic conditions and be more competitive than the other 3 modified algorithms.
URI: https://hdl.handle.net/20.500.14279/30820
ISBN: 9781728190488
DOI: 10.1109/SSCI50451.2021.9659933
Rights: © IEEE
Type: Conference Papers
Affiliation : Univsersity of Geosciences 
China University of Geosciences 
De Montfort University 
University of Cyprus 
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