Waveform inversion with an improved differential evolution algorithm

While the number of the parameters in waveform inversion is large, the conventional global optimization methods often lose their effectiveness and advantages. In this paper, an improved differential evolution (DE) algorithm is proposed. For DE operator selection, in the light of co-evolutionary, we decompose the complex problem into some subcomponents and introduce a local fitness function for each subcomponent. Then a quasi-next generation is selected one subcomponent by one subcomponent according to the local fitness values. However, considering the interdependence among subcomponents, the final next generation is still selected according to the global fitness values. Therefore, the evolution direction of the problem is guided by the local fitness values and the global fitness value simultaneously. This improved DE algorithm with a co-evolutionary selection operator is called DE-CCS, which has fast convergence rate and is effective for high-dimensional optimization problems. DE-CCS is applied to the complex waveform inversion problems. Model and real data experiments demonstrate the effectiveness of DE-CCS.