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融合协同进化的多约束卫星追逃博弈优化方法.

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Název: 融合协同进化的多约束卫星追逃博弈优化方法. (Chinese)
Alternate Title: Integrated cooperative co-evolutionary optimization method for multi-constraint satellite pursuit-evasion game. (English)
Autoři: 韩昊东, 王俊琦, 马宸宇浩, 张 勃, 许旭升, 袁秋帆, 刘田青, 周大明
Zdroj: Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao; Jan2026, Vol. 48 Issue 1, p99-112, 14p
Témata: TRAJECTORY optimization, DIFFERENTIAL games, CONSTRAINED optimization, EVOLUTIONARY algorithms, METAHEURISTIC algorithms
Abstract (English): Traditional methods often exhibit low efficiency in addressing multi-objective and multi-constraint optimization problems, failing to meet the requirements of dynamic and complex environments. In this case, a cooperative co-evolution algorithm was proposed based on cooperative co-evolution mechanisms, zebra optimization algorithms, and differential game theory. A phased optimization strategy was adopted to dynamically and adaptively optimize trajectories and strategies, while a multi-population co-evolution mechanism was introduced to enhance global exploration capability and local convergence performance. Differential game theory was integrated to improve the stability and reliability of game strategies. Simulation results demonstrate that this method significantly improves mission completion efficiency under multi-constraint conditions. It effectively balances dynamic strategy adjustments for both pursuers and evaders, providing an effective solution for satellite pursuit-evasion games in space-based target reconnaissance and surveillance missions. [ABSTRACT FROM AUTHOR]
Abstract (Chinese): 针对传统方法在应对多目标、多约束优化时效率较低, 难以满足动态复杂环境下的需求的问题, 基于协同进化机制、斑马优化算法和微分对策理论, 提出了一种融合协同进化算法。通过采用分阶段优化策略对轨迹和策略进行动态适应性优化, 同时引入多种群协同进化机制, 增强了算法的全局探索能力和局部收敛性能, 并结合微分对策理论, 提升了博弈策略的稳定性和可靠性。仿真实验结果表明, 该方法在多约束条件下能够显著提高任务完成效率, 同时可兼顾追逃双方的动态策略调整, 为天基空间目标侦察监视任务中的卫星追逃博弈提供了有效的解决方案。 [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
Popis
Abstrakt:Traditional methods often exhibit low efficiency in addressing multi-objective and multi-constraint optimization problems, failing to meet the requirements of dynamic and complex environments. In this case, a cooperative co-evolution algorithm was proposed based on cooperative co-evolution mechanisms, zebra optimization algorithms, and differential game theory. A phased optimization strategy was adopted to dynamically and adaptively optimize trajectories and strategies, while a multi-population co-evolution mechanism was introduced to enhance global exploration capability and local convergence performance. Differential game theory was integrated to improve the stability and reliability of game strategies. Simulation results demonstrate that this method significantly improves mission completion efficiency under multi-constraint conditions. It effectively balances dynamic strategy adjustments for both pursuers and evaders, providing an effective solution for satellite pursuit-evasion games in space-based target reconnaissance and surveillance missions. [ABSTRACT FROM AUTHOR]
ISSN:10012486
DOI:10.11887/j.issn.1001-2486.24120041