Solving MRTA problem based on multi-strategy genetic algorithm
This paper proposes a multi-strategy genetic algorithm (DIHA-GA) to address the issues of local optima and low efficiency in solving multi-robot task allocation (MRTA) using genetic algorithm (GA). Firstly, a dual chromosome coding strategy was adopted to simplify the coding process. Secondly, the p...
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Editorial Office of Journal of XPU
2024-12-01
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| Series: | Xi'an Gongcheng Daxue xuebao |
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| Online Access: | http://journal.xpu.edu.cn/en/#/digest?ArticleID=1520 |
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| _version_ | 1849321883106803712 |
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| author | CHEN Haiyang LIU Yan DOU Wei HUANG Qi |
| author_facet | CHEN Haiyang LIU Yan DOU Wei HUANG Qi |
| author_sort | CHEN Haiyang |
| collection | DOAJ |
| description | This paper proposes a multi-strategy genetic algorithm (DIHA-GA) to address the issues of local optima and low efficiency in solving multi-robot task allocation (MRTA) using genetic algorithm (GA). Firstly, a dual chromosome coding strategy was adopted to simplify the coding process. Secondly, the population was divided into three parts to enhance the quality of chromosomes while maintaining randomness. Then, heuristic crossover operators were used to expand the search range of the solution and increase the algorithm′s ability to jump out of local optima. Finally, adaptive crossover probability and mutation probability were used to make the algorithm find the optimal solution faster. The results showed that in the cases of 20 and 40 tasks, compared to the hybrid particle swarm optimization (HPSO), the average distance of the proposed DIHA-GA is reduced by 14.46 m and 17.36 m, respectively, and the minimum distance is reduced by 14.89 m and 20.86 m, respectively. This indicates that the solution obtained by DIHA-GA is closer to the optimal solution. The average distance obtained by DIHA-GA in this article is reduced by 21.32 m and 18.73 m respectively compared to the improved ant colony optimization (IACO), and the minimum distance is reduced by 23.43 m and 22.32 m respectively. This is due to the premature convergence of IACO and its difficulty in jumping out of local optima. The effectiveness of DIHA-GA in solving MRTA problems has been verified through comparison. |
| format | Article |
| id | doaj-art-82bf33faf2ac433e9207d12c6272e225 |
| institution | Kabale University |
| issn | 1674-649X |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Editorial Office of Journal of XPU |
| record_format | Article |
| series | Xi'an Gongcheng Daxue xuebao |
| spelling | doaj-art-82bf33faf2ac433e9207d12c6272e2252025-08-20T03:49:37ZzhoEditorial Office of Journal of XPUXi'an Gongcheng Daxue xuebao1674-649X2024-12-01386768210.13338/j.issn.1674-649x.2024.06.010Solving MRTA problem based on multi-strategy genetic algorithmCHEN Haiyang0LIU Yan1DOU Wei2HUANG Qi3School of Electronics and Information, Xi’an Polytechnic University, Xi’an 710048, ChinaSchool of Electronics and Information, Xi’an Polytechnic University, Xi’an 710048, ChinaSchool of Electronics and Information, Xi’an Polytechnic University, Xi’an 710048, ChinaSchool of Electronics and Information, Xi’an Polytechnic University, Xi’an 710048, ChinaThis paper proposes a multi-strategy genetic algorithm (DIHA-GA) to address the issues of local optima and low efficiency in solving multi-robot task allocation (MRTA) using genetic algorithm (GA). Firstly, a dual chromosome coding strategy was adopted to simplify the coding process. Secondly, the population was divided into three parts to enhance the quality of chromosomes while maintaining randomness. Then, heuristic crossover operators were used to expand the search range of the solution and increase the algorithm′s ability to jump out of local optima. Finally, adaptive crossover probability and mutation probability were used to make the algorithm find the optimal solution faster. The results showed that in the cases of 20 and 40 tasks, compared to the hybrid particle swarm optimization (HPSO), the average distance of the proposed DIHA-GA is reduced by 14.46 m and 17.36 m, respectively, and the minimum distance is reduced by 14.89 m and 20.86 m, respectively. This indicates that the solution obtained by DIHA-GA is closer to the optimal solution. The average distance obtained by DIHA-GA in this article is reduced by 21.32 m and 18.73 m respectively compared to the improved ant colony optimization (IACO), and the minimum distance is reduced by 23.43 m and 22.32 m respectively. This is due to the premature convergence of IACO and its difficulty in jumping out of local optima. The effectiveness of DIHA-GA in solving MRTA problems has been verified through comparison.http://journal.xpu.edu.cn/en/#/digest?ArticleID=1520multi-robot task allocation (mrta)warehousing logisticsgenetic algorithm (ga)improved circle strategyhybrid particle swarm optimization (hpso)ant colony optimization (aco) |
| spellingShingle | CHEN Haiyang LIU Yan DOU Wei HUANG Qi Solving MRTA problem based on multi-strategy genetic algorithm Xi'an Gongcheng Daxue xuebao multi-robot task allocation (mrta) warehousing logistics genetic algorithm (ga) improved circle strategy hybrid particle swarm optimization (hpso) ant colony optimization (aco) |
| title | Solving MRTA problem based on multi-strategy genetic algorithm |
| title_full | Solving MRTA problem based on multi-strategy genetic algorithm |
| title_fullStr | Solving MRTA problem based on multi-strategy genetic algorithm |
| title_full_unstemmed | Solving MRTA problem based on multi-strategy genetic algorithm |
| title_short | Solving MRTA problem based on multi-strategy genetic algorithm |
| title_sort | solving mrta problem based on multi strategy genetic algorithm |
| topic | multi-robot task allocation (mrta) warehousing logistics genetic algorithm (ga) improved circle strategy hybrid particle swarm optimization (hpso) ant colony optimization (aco) |
| url | http://journal.xpu.edu.cn/en/#/digest?ArticleID=1520 |
| work_keys_str_mv | AT chenhaiyang solvingmrtaproblembasedonmultistrategygeneticalgorithm AT liuyan solvingmrtaproblembasedonmultistrategygeneticalgorithm AT douwei solvingmrtaproblembasedonmultistrategygeneticalgorithm AT huangqi solvingmrtaproblembasedonmultistrategygeneticalgorithm |