Direction Guided Cooperative Coevolutionary Differential Evolution Algorithm for Cognitive Modelling of Ray Tracing in Separable High Dimensional Space

摘要

By simulating how our human brain solves complex and conceptual problems, cognitive systems have been successfully applied in a wide range of applications. In this paper, a cognitive modelling based inversion method, the direction guided differential evolution with cooperative coevolutionary mutation operator (DG-DECCM) algorithm, is proposed to trace the ray path of the seismic waves. The DE algorithm seeks optimal solutions by simulating the natural species evolution processes and makes the individuals become optimal. Classical ray tracing methods were time consuming and inefficiency. The proposed algorithm is suitable for the high and super high dimensional separable model space. It treats the emergent angles of the reflection points as genes of an individual. We introduce a sign function to guide the direction of the mutation and propose two kinds of stopping criteria for effective iteration to speed up the computation. For the complex velocity model, the local optimization methods based on gradient are time consuming to converge or may converge to local minimum but not the optimal value. The proposed global DE algorithm, however, will obtain a global optimum solution more efficiently and has higher convergence rate.