Multi-objective evolutionary algorithm based on RBF network for solving the stochastic vehicle routing problem

• Main Authors: NIU, Yunyun, SHAO, Jie, XIAO, Jianhua, SONG, Wen, CAO, Zhiguang
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: 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; Artificial Intelligence and Robotics; Operations Research, Systems Engineering and Industrial Engineering; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/8208; https://ink.library.smu.edu.sg/context/sis_research/article/9211/viewcontent/Multi_obj_evolutionary_algor_av__1_.pdf
• Institution: Singapore Management University

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.