Multi-objective evolutionary algorithm based on RBF network for solving the stochastic vehicle routing problem
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 se...
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| Vydané v: | Information sciences Ročník 609; s. 387 - 410 |
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01.09.2022
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| 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 network (RBFN) is exploited to learn the potential knowledge of individuals, generate hypothesis 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. |
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| AbstractList | 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 network (RBFN) is exploited to learn the potential knowledge of individuals, generate hypothesis 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. |
| Author | Song, Wen Shao, Jie Cao, Zhiguang Niu, Yunyun Xiao, Jianhua |
| Author_xml | – sequence: 1 givenname: Yunyun surname: Niu fullname: Niu, Yunyun organization: School of Information Engineering, China University of Geosciences in Beijing, Beijing 100083, China – sequence: 2 givenname: Jie surname: Shao fullname: Shao, Jie organization: School of Information Engineering, China University of Geosciences in Beijing, Beijing 100083, China – sequence: 3 givenname: Jianhua surname: Xiao fullname: Xiao, Jianhua email: jhxiao@nankai.edu.cn organization: Research Center of Logistics, Nankai University, Tianjin 300071, China – sequence: 4 givenname: Wen surname: Song fullname: Song, Wen organization: Institute of Marine Science and Technology, Shandong University, Shandong 250100, China – sequence: 5 givenname: Zhiguang surname: Cao fullname: Cao, Zhiguang organization: Singapore Institute of Manufacturing Technology, Singapore 138634, Singapore |
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| Cites_doi | 10.3390/info9070167 10.1109/4235.996017 10.1007/s00500-019-04312-9 10.1287/opre.40.3.574 10.1023/A:1007677805582 10.1023/A:1022643204877 10.1142/S0219649214500221 10.1007/s10479-015-1949-7 10.1080/00401706.1971.10488811 10.1287/opre.35.2.254 10.1016/j.knosys.2021.107378 10.1007/978-3-319-93025-1_4 10.1109/TEVC.2014.2308305 10.1016/S0952-1976(02)00011-8 10.1016/j.neucom.2011.03.027 10.1016/j.procs.2014.09.077 10.1109/5.58326 10.1016/S0888-3270(03)00080-3 10.1016/j.ejor.2005.12.029 10.1007/s10710-005-6164-x |
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| Keywords | Learnable evolution model Radial basis function network Multi-objective evolutionary algorithm Vehicle routing problem Stochastic demand |
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