Aiming at the problems of basic genetic algorithm in the field of path planning to system resilience recovery such as excessive randomness of initial population, slow convergence, low efficiency of evolution operator, and poor population diversity, this paper uses quotient model to measure resilience, uses overall task importance to measure system performance, and proposes an improved genetic algorithm on initial population and evolutionary operation. Improved genetic algorithm (IHGA) proposes a new greedy model that considers system node tasks importance, travel time, and maintenance time, which uses greedy ideas to generate partial high-quality initial population. And a new operator is also designed as intra-group head-to-head mutation operator (IHMO) to control the evolution to be more determinate and less ineffectively random. The simulation results in three cases show that the IHGA overcomes the defects and can better effectively recover system resilience with comparison to basic genetic algorithm (BGA) and multi-chromosome genetic algorithm (MCGA). Specially, it has obviously better optimal solution, convergence, and stability, especially in the harsh conditions as shorter repair time, more and unbalanced demands for spare parts, which shows the IHGA has great value to deal with measurement and control of system resilience recovery in practice.

针对路径规划领域的基本遗传算法对系统弹性恢复的初始种群随机性过大、收敛速度慢、进化算子效率低、种群多样性差等问题，本文采用商模型衡量弹性，利用总体任务重要性来衡量系统性能，并提出了一种改进的初始种群和进化操作的遗传算法。改进遗传算法（IHGA）提出了一种新的贪心模型，该模型考虑了系统节点任务的重要性、旅行时间和维护时间，利用贪心思想生成了部分高质量的初始种群。并且还设计了一个新的算子作为组内头对头变异算子（IHMO），以控制进化更加确定，减少无效随机。三种情况下的仿真结果表明，与基本遗传算法（BGA）和多染色体遗传算法（MCGA）相比，IHGA克服了这些缺陷，能够更好地有效恢复系统弹性。特别是它具有明显更好的最优解、收敛性和稳定性，特别是在维修时间较短、备件需求较多且不平衡的恶劣条件下，表明IHGA在应对系统弹性恢复测控方面具有重要价值。实践。