Research of parallel genetic algorithm for shallow S-wave velocity structure inversion

A coarse grained parallel genetic algorithm based on personal computer cluster is proposed to inverse shallow S-wave velocity structure of actual engineering sites, in order to solve the disadvantage of traditional genetic algorithm, such as easy trap into extreme and computed long time. The simulated annealing algorithm and parallel technique message passing interface (MPI) are adopted to implement the coarse grained parallel computer. The subpopulations are collaboratively optimized through individual migration strategy and the dynamic populations are adopted to balance the computing load. The shallow shear wave velocity structures of two examples and the actual engineering sites are inversed through a 4 node PC cluster test system. The simple models converge to the optimum solution and the average errors of the inversed results of the actual engineering sites are all less than 20% compared with the PS logging data. The computing speed increases rapidly, and the results inversed by parallel genetic algorithm are better than those inversed by genetic algorithm. The coarse grained parallel genetic algorithm designed increases the evolutionary rate effectively and the algorithm has a good parallel efficiency. The locally search capability in inversion is improved and the inversed results are better. The results show that the algorithm proposed can be used to inverse shallow S-wave velocity structure of engineering site.