Publication Type
Journal Article
Version
acceptedVersion
Publication Date
7-2022
Abstract
Solving the multi-objective vehicle routing problem with stochastic demand (MO-VRPSD) is challenging due to its non-deterministic property and conflicting objectives. Most multi -objective evolutionary algorithm dealing with this problem update current population without any guidance from previous searching experience. In this paper, a multi -objective evolutionary algorithm based on artificial neural networks is proposed to tackle the MO-VRPSD. Particularly, during the evolutionary process, a radial basis function net-work (RBFN) is exploited to learn the potential knowledge of individuals, generate hypoth-esis and instantiate hypothesis. The RBFN evaluates individuals with different scores and generates new individuals with higher quality while taking into account the non -dominated relationship between individuals. Moreover, integrated with a specific non -dominated sorting strategy, i.e., ENS-SS, along with several effective heuristic operations, the proposed algorithm performs favorably for solving the MO-VRPSD. The experimental results based on the modified Solomon benchmark instances verified the effectiveness of the respective components, and the superiority to other multi-objective evolutionary algorithms. (c) 2022 Elsevier Inc. All rights reserved.
Keywords
Vehicle routing problem;Stochastic demand;Learnable evolution model;Multi -objective evolutionary algorithm;Radial basis function network;Vehicle routing problem;Stochastic demand;Learnable evolution model;Multi -objective evolutionary algorithm;Radial basis function network
Discipline
Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering | Theory and Algorithms
Research Areas
Intelligent Systems and Optimization
Publication
Information Sciences
Volume
609
First Page
387
Last Page
410
ISSN
0020-0255
Identifier
10.1016/j.ins.2022.07.087
Publisher
Elsevier
Citation
NIU, Yunyun; SHAO, Jie; XIAO, Jianhua; SONG, Wen; and CAO, Zhiguang.
Multi-objective evolutionary algorithm based on RBF network for solving the stochastic vehicle routing problem. (2022). Information Sciences. 609, 387-410.
Available at: https://ink.library.smu.edu.sg/sis_research/8208
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.ins.2022.07.087
Included in
Artificial Intelligence and Robotics Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Theory and Algorithms Commons