Advanced Phasmatodea Population Evolution Algorithm for Capacitated Vehicle Routing Problem
Capacitated Vehicle Routing Problem (CVRP) is difficult to solve by the traditional precise methods in the transportation area. The metaheuristic algorithm is often used to solve CVRP and can obtain approximate optimal solutions. Phasmatodea population evolution algorithm (PPE) is a recently propose...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/9241112 |
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| _version_ | 1849305206843506688 |
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| author | Jiawen Zhuang Shu-Chuan Chu Chia-Cheng Hu Lyuchao Liao Jeng-Shyang Pan |
| author_facet | Jiawen Zhuang Shu-Chuan Chu Chia-Cheng Hu Lyuchao Liao Jeng-Shyang Pan |
| author_sort | Jiawen Zhuang |
| collection | DOAJ |
| description | Capacitated Vehicle Routing Problem (CVRP) is difficult to solve by the traditional precise methods in the transportation area. The metaheuristic algorithm is often used to solve CVRP and can obtain approximate optimal solutions. Phasmatodea population evolution algorithm (PPE) is a recently proposed metaheuristic algorithm. Given the shortcomings of PPE, such as its low convergence precision, its nature to fall into local optima easily, and it being time-consuming, we propose an advanced Phasmatodea population evolution algorithm (APPE). In APPE, we delete competition, delete conditional acceptance and correspondingevolutionary trend update, and add jump mechanism, history-based searching, and population closing moving. Deleting competition and conditional acceptance and correspondingevolutionary trend update can shorten PPE running time. Adding a jump mechanism makes PPE more likely to jump out of the local optimum. Adding history-based searching and population closing moving improves PPE’s convergence accuracy. Then, we test APPE by CEC2013. We compare the proposed APPE with differential evolution (DE), sparrow search algorithm (SSA), Harris Hawk optimization (HHO), and PPE. Experiment results show that APPE has higher convergence accuracy and shorter running time. Finally, APPE also is applied to solve CVRP. From the test results of the instances, APPE is more suitable to solve CVRP. |
| format | Article |
| id | doaj-art-0242fbb971824c79b86476ecb0bd437d |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-0242fbb971824c79b86476ecb0bd437d2025-08-20T03:55:32ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/9241112Advanced Phasmatodea Population Evolution Algorithm for Capacitated Vehicle Routing ProblemJiawen Zhuang0Shu-Chuan Chu1Chia-Cheng Hu2Lyuchao Liao3Jeng-Shyang Pan4School of Computer Science and MathematicsCollege of Computer Science and EngineeringCollege of Artificial IntelligenceSchool of Computer Science and MathematicsSchool of Computer Science and MathematicsCapacitated Vehicle Routing Problem (CVRP) is difficult to solve by the traditional precise methods in the transportation area. The metaheuristic algorithm is often used to solve CVRP and can obtain approximate optimal solutions. Phasmatodea population evolution algorithm (PPE) is a recently proposed metaheuristic algorithm. Given the shortcomings of PPE, such as its low convergence precision, its nature to fall into local optima easily, and it being time-consuming, we propose an advanced Phasmatodea population evolution algorithm (APPE). In APPE, we delete competition, delete conditional acceptance and correspondingevolutionary trend update, and add jump mechanism, history-based searching, and population closing moving. Deleting competition and conditional acceptance and correspondingevolutionary trend update can shorten PPE running time. Adding a jump mechanism makes PPE more likely to jump out of the local optimum. Adding history-based searching and population closing moving improves PPE’s convergence accuracy. Then, we test APPE by CEC2013. We compare the proposed APPE with differential evolution (DE), sparrow search algorithm (SSA), Harris Hawk optimization (HHO), and PPE. Experiment results show that APPE has higher convergence accuracy and shorter running time. Finally, APPE also is applied to solve CVRP. From the test results of the instances, APPE is more suitable to solve CVRP.http://dx.doi.org/10.1155/2022/9241112 |
| spellingShingle | Jiawen Zhuang Shu-Chuan Chu Chia-Cheng Hu Lyuchao Liao Jeng-Shyang Pan Advanced Phasmatodea Population Evolution Algorithm for Capacitated Vehicle Routing Problem Journal of Advanced Transportation |
| title | Advanced Phasmatodea Population Evolution Algorithm for Capacitated Vehicle Routing Problem |
| title_full | Advanced Phasmatodea Population Evolution Algorithm for Capacitated Vehicle Routing Problem |
| title_fullStr | Advanced Phasmatodea Population Evolution Algorithm for Capacitated Vehicle Routing Problem |
| title_full_unstemmed | Advanced Phasmatodea Population Evolution Algorithm for Capacitated Vehicle Routing Problem |
| title_short | Advanced Phasmatodea Population Evolution Algorithm for Capacitated Vehicle Routing Problem |
| title_sort | advanced phasmatodea population evolution algorithm for capacitated vehicle routing problem |
| url | http://dx.doi.org/10.1155/2022/9241112 |
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