*3.1. Experimental Environment*

To verify the feasibility and efficiency of the algorithm proposed in this article, several groups of comparative experiments were carried out by using the new ASGS-CWOA-BP method, the classification method GA-BP based on genetic algorithm optimization and the BP neural network, the classification method PSO-BP based on particle swarm optimization and the BP neural network, the classification method LWCA-BP based on the wolf pack optimization algorithm with the leadership strategy and the BP neural network, and the original BP neural network based on gradient descent and random forest (RF).

Table 1 shows the numerical experimental data based on six-dimensional feature vectors from 358 samples, and Table 2 lists the parameters of the six classification methods for coal and gangue images. The comparative experiments were run on a computer equipped with a CPU (AMD A6-3400m APU with RadeonTM HD Graphics 1.40 GHz), 12.0 GB of memory (11.5 GB available) and Windows 7 (64 bit). To prove the good performance of the proposed algorithm, optimization calculations were run 30 times on the sample feature vectors for testing, and the classification algorithm mentioned above were also tested.


