A New Highly Efficient Differential Evolution Scheme and Its Application to Waveform Inversion

In this letter, a new differential evolution (DE) algorithm is proposed and applied to waveform inversion. The traditional evolution strategy of this algorithm is not efficient because it treats the individuals in a population equally and evolves all of them in each generation. In order to overcome this shortcoming, we propose a new population evolution strategy (PES) to decrease the population size based on the differences among individuals during an evolution process. We embed the new strategy into the cooperative coevolutionary DE (CCDE) and obtain a new highly efficient DE (HEDE). We apply this new algorithm to waveform inversion experiments of both synthetic and real seismic data to test its performance and demonstrate its validity. The results have clearly shown that, under the same inversion precision, the HEDE can reduce the runtime by about 50% compared with the CCDE.