Theoretical Improvements and Applications Research of Genetic Algorithms in Collaborative Optimization

随着复杂系统建模与仿真技术的快速发展，协同优化算法在军事指挥、智能制造等领域的应用需求持续增长。本文系统梳理了改进遗传算法在协同的方法论演进与技术突破，重点分析其在多目标动态优化、混合智能架构、自适应参数调节等方面的创新实践。通过典型应用案例的对比研究，揭示算法改进路径与工程实现的内在关联，并探讨数字孪生、边缘计算等新兴技术带来的发展机遇。最后提出算法收敛理论体系构建、超大规模协同优化等未来研究方向。With the rapid development of complex system modeling and simulation technologies, the application demands for collaborative optimization algorithms in fields such as military command and intelligent manufacturing continue to grow. This paper systematically reviews the methodological evolution and technical breakthroughs of improved genetic algorithms (GAs) in collaborative optimization, focusing on innovative practices in multi-objective dynamic optimization, hybrid intelligent architectures, and adaptive parameter adjustment. Through comparative studies of typical application cases, the intrinsic connections between algorithm improvement paths and engineering implementations are revealed, while development opportunities brought by emerging technologies such as digital twin and edge computing are explored. Finally, future research directions including the construction of algorithm convergence theoretical systems and ultra-large-scale collaborative optimization are proposed.