BP neural network and improved differential evolution for transient electromagnetic inversion Abstract   In the transient electromagnetic (TEM) inversion, the BP neural network method has high efficiency owing to avoid the complicated forward model calculation in every iteration. The global optimization ability of the differential evolution Algorithm (DE) is adopted for amending BPu0027s sensitive to initial parameters. A chaotic mutation and crossover with constraint factor DE (CCDE) is proposed in improving the global optimization ability. The CCDEBP algorithm performance is validated by two typical testing functions and then by two geoelectric models inversion. The results show that the CCDEBP method has better inversion speed, accuracy and stability yet with higher fitting degree. It is feasible in geophysical inverse applications.

An Adaptive Differential Evolution Algorithm Based on Fuzzy Modeling The appropriate parameter setting can substantially determine the performance of differential evolution (DE), so parameter design is a very crucial and challenging task in DE. In response to the realistic demands, a novel adjust strategy for adaptive parameter is developed for DE in this paper. By way of the strategy of fuzzy modeling, the phases of optimization are designed as follows, i.e., exploration, exploitation and convergence. The adaptive adjust of F and CR, the control parameters, is determined by the phases of optimization. Meanwhile, an auxiliary movement technique is designed for the convergence population. This technique will help the best individual to avoid the risk of falling into the potential local optima. The proposed algorithm, namely FMDErand1, has been assessed under eight unimodal and multimodal benchmark functions. Results from experiments illustrate that the proposed FMDErand1 is a promising optimization algorithm which will greatly enhance the performance on effectiveness and dynamic.