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For the problems of uneven initial population and premature convergence in the basic grey wolf algorithm, grey wolf optimization(GWO) algorithm is improved from three aspects based on chaos theory, and a chaotic grey wolf optimization(CGWO) is proposed for determining the minimum safety factor of the slope. Firstly, an improved Tent chaotic mapping is used to improve the initial population diversity; Secondly, a chaotic perturbation strategy is used to avoid the algorithm from falling into a local optimal; Finally, a parametric chaotic non-linear adjustment mechanism is introduced to balance the global exploitation and local exploration arithmetic of the algorithm. Simulation results of 13 benchmark test functions show that the improved algorithm has a stronger integrated optimization search performance compared with the basic GWO, WOA, PSO and SCA. Selecting the ACADS side slope assessment questions for computation and analysis, the CGWO algorithm shows a high computational accuracy and convergence speed, and can effectively search for the minimum safety factor of complex stratified slopes. Compared with the finite element strength reduction method, the method has the advantages of easy operation and easy setting of the search area. [ABSTRACT FROM AUTHOR]

A chaotic parameter-optimized radial basis function (RBF) forecasting model was proposed. The chaos theory was used to obtain the embedded dimension and delay time of the ENPEMF， and the obtained parameters were used to optimize the RBF neural network. Finally， the trained optimized-RBF was utilized to forecast the strength trend of 14 d ENPEMF data. Numerical simulation results show that the improved RBF model could forecast the Rossler time series well with small error. Applying the improved RBF algorithm to the ENPEMF data before Ms7.0 earthquake in Lushan， it can effectively forecast the ENPEMF intensity trend 14 d before earthquake. The forecasting effect and accuracy are significantly better than that of the traditional RBF algorithm， which is expected to provide support for electromagnetic monitoring and analysis before earthquakes and geological disasters. [ABSTRACT FROM AUTHOR]