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Sine cosine algorithm (SCA) is a recently developed optimization technique, which uses sine function and cosine function as operators to find the global optimal solution. However, proper parameter setting is a challenging task. Only using the number of iterations to adjust the algorithm parameters cannot fully reflect the convergence information in the evolution process, so SCA lacks the adaptability in solving different optimization problems. To address this issue, a cloud model based sine cosine algorithm (CSCA) is proposed. In CSCA, the cloud model is used to adjust the control parameter adaptively while keeping SCA algorithm framework unchanged. The performance of the presented CSCA method is evaluated using 13 benchmark test functions with different dimensions. Experimental results demonstrate that the proposed algorithm is superior to other SCA variants in terms of robustness and scalability.

In view of the nonlinear characteristics of gas emission in a coal working face, a prediction method is proposed based on cuckoo search algorithm optimized BP neural network (CSBP). In the CSBP algorithm, the cuckoo search is adopted to optimize weight and threshold parameters of BP network, and obtains the global optimal solutions. Furthermore, the twelve main affecting factors of the gas emission in the coal working face are taken as input vectors of CSBP algorithm, the gas emission is acted as output vector, and then the prediction model of BP neural network with optimal parameters is established. The results show that the CSBP algorithm has batter generalization ability and higher prediction accuracy, and can be utilized effectively in the prediction of coal mine gas emission.