A dynamic space reduction ant colony optimization for capacitated vehicle routing problem
As a typical meta-heuristic algorithm, ant colony optimization (ACO) has achieved good results in solving discrete combinatorial optimization problems. However, it suffers from poor solutions and the drawback of easily being trapped in local optima. This paper presents a new type of ACO called “dyna...
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| Vydáno v: | Soft computing (Berlin, Germany) Ročník 26; číslo 17; s. 8745 - 8756 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.09.2022
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| ISSN: | 1432-7643, 1433-7479 |
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| Abstract | As a typical meta-heuristic algorithm, ant colony optimization (ACO) has achieved good results in solving discrete combinatorial optimization problems. However, it suffers from poor solutions and the drawback of easily being trapped in local optima. This paper presents a new type of ACO called “dynamic space reduction ant colony optimization” (DSRACO) to solve the capacitated vehicle routing problem, which is a typical nondeterministic polynomial-hard optimization problem. In DSRACO, ACO is integrated with a unique dynamic space reduction method, an elite enhanced mechanism, and large-scale neighborhood search methods to improve the quality of the solution. The performance of DSRACO is evaluated using 73 well-known benchmark instances in comparison with ACO and three other cutting-edge algorithms. The experimental results show that DSRACO can solve CVRP with a satisfactory result. |
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| AbstractList | As a typical meta-heuristic algorithm, ant colony optimization (ACO) has achieved good results in solving discrete combinatorial optimization problems. However, it suffers from poor solutions and the drawback of easily being trapped in local optima. This paper presents a new type of ACO called “dynamic space reduction ant colony optimization” (DSRACO) to solve the capacitated vehicle routing problem, which is a typical nondeterministic polynomial-hard optimization problem. In DSRACO, ACO is integrated with a unique dynamic space reduction method, an elite enhanced mechanism, and large-scale neighborhood search methods to improve the quality of the solution. The performance of DSRACO is evaluated using 73 well-known benchmark instances in comparison with ACO and three other cutting-edge algorithms. The experimental results show that DSRACO can solve CVRP with a satisfactory result. |
| Author | Wang, Peng Dong, Huachao Cai, Jinsi Sun, Siqing |
| Author_xml | – sequence: 1 givenname: Jinsi surname: Cai fullname: Cai, Jinsi organization: School of Marine Science and Technology, Northwestern Polytechnical University – sequence: 2 givenname: Peng orcidid: 0000-0002-8745-320X surname: Wang fullname: Wang, Peng email: wangpeng305@nwpu.edu.cn organization: School of Marine Science and Technology, Northwestern Polytechnical University – sequence: 3 givenname: Siqing surname: Sun fullname: Sun, Siqing organization: School of Artificial Intelligence and Automation, Huazhong University of Science and Technology – sequence: 4 givenname: Huachao surname: Dong fullname: Dong, Huachao organization: School of Marine Science and Technology, Northwestern Polytechnical University |
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| Cites_doi | 10.1287/mnsc.6.1.80 10.1007/s10479-005-3971-7 10.1007/s00170-007-1203-9 10.1016/j.ins.2019.03.070 10.1016/j.omega.2017.05.002 10.1007/978-0-387-77778-8_7 10.1155/2012/726564 10.1016/j.endm.2017.03.030 10.1007/s10107-007-0178-5 10.1016/j.eswa.2016.05.023 10.1109/3477.484436 10.1109/ACCESS.2019.2925831 10.1016/j.ejor.2011.07.037 10.1016/j.eswa.2007.11.060 10.1016/j.jocs.2017.12.012 10.1016/j.asoc.2009.07.003 10.1016/j.asoc.2019.105728 10.1016/j.swevo.2011.10.002 10.1109/TITS.2016.2542264 10.1016/j.ejor.2011.06.006 10.1016/j.swevo.2013.05.005 10.1057/palgrave.jors.2600357 10.1007/978-1-4684-2001-2_9 10.1109/CEC.2017.7969511 10.1109/CIACT.2017.7977376 10.1109/ICCSA.2014.39 10.1109/CEC.2017.7969412 |
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| Keywords | Ant colony optimization Discrete combinatorial optimization problem Capacitated vehicle routing problem Evolutionary algorithm |
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