【摘要】 通过重构非线性惯性权重函数和引入"加速因子",对传统粒子群优化算法的收敛性进行改进。同时基于消息传递平台对算法进行主从式并行改进,编程实现基于普通计算机机群系统分布式存储并行模式的大型地下工程并行优化反演分析。算例分析表明,改进的粒子群优化算法其收敛性能得到显著改善,并行改进策略可显著加快反演速度和提高计算效率。探讨围岩松动损伤劣化、监测数据可靠性、并行粒度和负载均衡等并行优化反演分析中所面临的主要问题及其对计算精度和效率的影响,并提出有效解决方案,为大型地下工程的参数反演和动态优化设计提供一种新思路。更多还原

【Abstract】 The conventional particle swarm optimization is improved by composing a nonlinear inertia weight function and importing in an acceleration factor,which can enhance the convergence and efficiency of computation.At the same time,the improved particle swarm optimization is improved again by message passing interface(MPI)-based master-slave parallel framework.The back analysis process of large-scale underground engineering which based on the ordinary computer fleet system distributed-storage parallel mode is compiled with Fortran language.According to distributed-memory parallel mode,the parallel computation can be conducted and completed using computer cluster networks;thus considerably reduce the cost and enhance the efficiency of computation.The results indicate that the improved particle swarm optimization is efficient.Moreover,the influences of excavation damage of surrounding rock mass,reliability of measured data,parallel granularity and load balance on computational efficiency and accuracy of back analysis in underground engineering are briefly discussed.The proposed improved method and the rational recommendations provide the back analysis of parameters and dynamic optimal design of underground engineering with a new idea.更多还原