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<p>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.</p>

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Optimization of Combat Resource Allocation Based on Restricted Tournament Selection Social Genetic Algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月23日

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  • Shandong Yuan,
  • Yun Ren,
  • Han Zhou,
  • Yongjing Cheng,
  • Kai Yan

摘要

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.