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In this paper, improved genetic algorithm (GA) is used to search Chang'e-3 optimal landing preparative orbit perilune. Chang'e-3 lander as an object, the initial and final state of the lander and the kinetic equations in the lunar gravitational field are determined by reversely reasoning from the predetermined landing site in the main reduction stage. The lander motion state is non-linear and continuous, so this method takes advantage of genetic algorithms and discretized track to achieve the optimal location of perilune, with the help of Kepler's third law and the law of inertia. Simulation results show that the improved genetic algorithm has the better performance in convergence speed and can be applied to search for the global optimal location of perilune.

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The vibration signal of rolling bearing fault is nonlinear and nonstationary under the interference of background noise, and it is difficult to extract fault features from it. When feature mode decomposition is used to analyze signals, prior parameter settings can easily affect the decomposition results. Therefore, a fault feature extraction method based on improved whale optimization algorithm is proposed to optimize feature modal decomposition parameters. The improved WOA integrates Lévy flight and adaptive weight, and envelope entropy is used as fitness function to optimize feature modal decomposition parameters. The feature mode decomposition of the original signal is performed using the optimal combination of parameters to obtain multiple IMF components. The optimal IMF component envelope demodulation analysis is selected according to the kurtosis value, and the fault feature is extracted through the envelope spectrum. Comparing the LMWOA method with PSO and WOA methods by simulated and experimental signals, the results show that the optimization speed of LMWOA is faster than that of other methods. Compared with CEEMD, VMD, and FMD methods, the improved WOA-FMD method has higher fault feature ratio and can accurately extract fault features under noise interference. This method can effectively solve the parameter adaptive ability and improve the accuracy of fault diagnosis, which has practical significance.

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