Optimization of Combat Resource Allocation Based on Restricted Tournament Selection Social Genetic Algorithm
Abstract To tackle the challenge of combat resource allocation problem (CRAP), especially under resource constraints, the dilemma between the efficiency of combat resource utilization and the efficiency of problem-solving. We propose a novel genetic algorithm that integrates a restricted tournament...
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| Format: | Article |
| Language: | English |
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Springer
2025-08-01
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| Series: | International Journal of Computational Intelligence Systems |
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| Online Access: | https://doi.org/10.1007/s44196-025-00958-6 |
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| _version_ | 1849225888063815680 |
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| author | Shandong Yuan Yun Ren Han Zhou Yongjing Cheng Kai Yan |
| author_facet | Shandong Yuan Yun Ren Han Zhou Yongjing Cheng Kai Yan |
| author_sort | Shandong Yuan |
| collection | DOAJ |
| description | Abstract To tackle the challenge of combat resource allocation problem (CRAP), especially under resource constraints, the dilemma between the efficiency of combat resource utilization and the efficiency of problem-solving. We propose a novel genetic algorithm that integrates a restricted tournament selection strategy with sociological principles, named the restricted tournament selection social genetic algorithm (RTS2GA). Initially, we develop a comprehensive model for the allocation of combat resources that takes into account multiple constraints. Then, building upon the traditional Genetic Algorithm, we introduce a novel selection strategy known as restricted tournament selection to enhance the diversity of the algorithm’s population. In addition, we innovatively incorporate the concept of ‘group effect’ from sociology, adding a socialization operator to the algorithm to accelerate convergence and improve the quality of optimal solutions. Comprehensive evaluation confirms RTS2GA’s trade-off profile: though incurring added computational costs, it achieves competitive convergence speed (marginally behind PSO/MPSO; comparable to GA/DE/GA-APSO) while establishing definitive superiority in global optimization across all five benchmarks. |
| format | Article |
| id | doaj-art-076adebd7cd5401d8b7da51dffcf4992 |
| institution | Kabale University |
| issn | 1875-6883 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj-art-076adebd7cd5401d8b7da51dffcf49922025-08-24T11:49:34ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832025-08-0118111810.1007/s44196-025-00958-6Optimization of Combat Resource Allocation Based on Restricted Tournament Selection Social Genetic AlgorithmShandong Yuan0Yun Ren1Han Zhou2Yongjing Cheng3Kai Yan4National University of Defense TechnologyInformation Support Force Engineering UniversityInformation Support Force Engineering UniversityInformation Support Force Engineering UniversityNational University of Defense TechnologyAbstract To tackle the challenge of combat resource allocation problem (CRAP), especially under resource constraints, the dilemma between the efficiency of combat resource utilization and the efficiency of problem-solving. We propose a novel genetic algorithm that integrates a restricted tournament selection strategy with sociological principles, named the restricted tournament selection social genetic algorithm (RTS2GA). Initially, we develop a comprehensive model for the allocation of combat resources that takes into account multiple constraints. Then, building upon the traditional Genetic Algorithm, we introduce a novel selection strategy known as restricted tournament selection to enhance the diversity of the algorithm’s population. In addition, we innovatively incorporate the concept of ‘group effect’ from sociology, adding a socialization operator to the algorithm to accelerate convergence and improve the quality of optimal solutions. Comprehensive evaluation confirms RTS2GA’s trade-off profile: though incurring added computational costs, it achieves competitive convergence speed (marginally behind PSO/MPSO; comparable to GA/DE/GA-APSO) while establishing definitive superiority in global optimization across all five benchmarks.https://doi.org/10.1007/s44196-025-00958-6Combat resource allocation problemGenetic algorithmRestricted tournament selectionGroup effectSocialization operator |
| spellingShingle | Shandong Yuan Yun Ren Han Zhou Yongjing Cheng Kai Yan Optimization of Combat Resource Allocation Based on Restricted Tournament Selection Social Genetic Algorithm International Journal of Computational Intelligence Systems Combat resource allocation problem Genetic algorithm Restricted tournament selection Group effect Socialization operator |
| title | Optimization of Combat Resource Allocation Based on Restricted Tournament Selection Social Genetic Algorithm |
| title_full | Optimization of Combat Resource Allocation Based on Restricted Tournament Selection Social Genetic Algorithm |
| title_fullStr | Optimization of Combat Resource Allocation Based on Restricted Tournament Selection Social Genetic Algorithm |
| title_full_unstemmed | Optimization of Combat Resource Allocation Based on Restricted Tournament Selection Social Genetic Algorithm |
| title_short | Optimization of Combat Resource Allocation Based on Restricted Tournament Selection Social Genetic Algorithm |
| title_sort | optimization of combat resource allocation based on restricted tournament selection social genetic algorithm |
| topic | Combat resource allocation problem Genetic algorithm Restricted tournament selection Group effect Socialization operator |
| url | https://doi.org/10.1007/s44196-025-00958-6 |
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