**1. Introduction**

Removing gangue from raw coal is conducive to improving the efficiency of coal utilization and reducing environmental pollution. Therefore, the identification of gangue and coal blocks is a necessary step for the efficient removal of coal gangue in coal production, which is of grea<sup>t</sup> significance for environmental protection [1,2]. Because of its obvious advantages of resource saving, low cost, the simple processing system and convenient maintenance, image recognition technology for the separation of coal gangue has been widely studied and applied in the literature [3–7]. Researchers have developed a variety of image recognition methods for coal gangue image recognition, which contribute to research into coal gangue separation technology based on image recognition and have laid a foundation for further research. However, these methods have their shortcomings. For example, in [3], the proposed method resulted in a waste of computing resources and low time efficiency because it calculated 15 eigenvalues at the same time, although only the best five eigenvalues were actually used. In the study by Zhou et al. [4], the new method adopted a deep convolution neural network to address the task of online accurate and rapid identification of coal gangue, which required extensive computation. In other studies [5,6], although the recognition rate of the methods used was high, the artificially set threshold parameters played a decisive role in the intermediate calculation link, which depended on an extensive experimental statistical analysis or experience, resulting in the calculation being complex. The stability of performance was questionable, and the generalized ability to recognize coal gangue images with different characteristics in different regions was not

<sup>1</sup> R & D Department, Zhuhai Xinhe Technology Co., Ltd., Zhuhai 519600, China; or

necessarily high. The authors of [7], proposed a method that required a large number of samples generated by the introduction of the transfer learning method, suggesting that the method's generalization and universality are questionable and may be limited when the sample size is small.

Since it was proposed by the team of Rumelhart and McClelland in 1986, the BP (back propagation) neural network has been widely used in the field of image recognition [8] because of its simple structure and high calculation efficiency, such as in [9]. To solve the problem of remote sensing image classification, the authors established an improved BP neural network and set the dynamic training intensity to improve the learning speed and classification accuracy of the BP neural network classifier. However, determining the optimal training intensity consumes a large amount of computing resources; moreover, the sample set classification problems of different fields have different optimal training intensities due to the large differences in the sample characteristics, so the generalizability is questionable, probably resulting in this method being unsuitable for image coal gangue classification with a wide area and many sources. In another study [10], a small image recognition and classification method based on GA-BP was proposed, in which the combination of a genetic algorithm and the BP neural network gave full play to the nonlinear mapping ability of the neural network, resulting in strong learning ability and fast convergence speed. The experimental results showed that the new method had higher recognition accuracy and better performance than the traditional BP algorithm and the GA-BP algorithm. The authors of [11] adopted a hybrid algorithm combining a genetic algorithm and a back propagation algorithm, and the experimental results showed that this GA-BP algorithm had higher efficiency, robustness and practicability. The researchers in [12] proposed an epilepsy diagnosis method based on an improved genetic algorithm– optimized back propagation (IGA-BP) neural network and used this method to detect clinical epilepsy quickly and effectively. Liu et al. [13] used a genetic algorithm (GA) to construct a classification model based on the BP neural network to automatically identify the correlations in a multi-mode blog, and the experimental results showed that the classification model based on GA-BP was better than the traditional BP neural network. Yu et al. [14] considered the problem that a BP algorithm based on a gradient descent principle falls into the local minima, and thus, the classification of multispectral remote sensing images using spectral information cannot obtain ideal results. Yu et al. developed a new method combining feature texture knowledge with a BP neural network trained by particle swarm optimization (PSO). The experimental results showed that this method had improved classification accuracy. Ying et al. [15] proposed a method combining a random loss algorithm and particle swarm optimization (PSO-BP) for the recognition and classification of small images, which corrected the weights of the PSO algorithm based on the error back propagation adjustment of the traditional BP algorithm to establish the PSO-BP network model. The hidden layer unit of the PSO-BP network was improved by using the random loss algorithm, thus achieving faster operation speeds. The authors of [16,17] proposed variants of the PSO-BP combining a BP neural network and PSO to address the task of supervised classification of synthetic aperture radar (SAR) images and the problems of low accuracy and efficiency in traditional part classification methods. This kind of BP neural network method based on GA or PSO optimization has a certain reference value for coal gangue image classification. Unfortunately, the accuracy of image coal gangue classification was still not high, as shown by a comparative experiment in a follow-up paper. In addition, the BP neural network has also been applied in other fields. For example, in [18], the researchers proposed a metal surface defect classification method based on an improved bat algorithm to optimize the BP neural network, which was used to classify images of defects with different characteristics. The researchers in [19] proposed an automatic recognition method of cloud and precipitation particle shapes based on a BP neural network to solve the problem that the shape of cloud particle images measured by airborne cloud imaging probes (CIPs) cannot be automatically recognized.

Although these methods have some shortcomings, they have still contributed to the research into coal gangue separation technology based on image recognition and laid a foundation for further research. In 2007, Yang and his coauthors proposed a new swarm intelligence algorithm [20], which simulates the predation process of wolves to solve complex nonlinear optimization problems. Due to its superior performance, it has been widely used in various fields and has been continuously developed and improved. For example, in [21], the author proposed a novel and effective opposite wolf pack algorithm to estimate the parameters of a Lorenz chaotic system. In another study [22], the improved wolf pack search algorithm was used to calculate the quasi-optimal trajectory of a rotor UAV in complex three-dimensional space. Moreover, in [23], the wolf pack algorithm was used to find the root of the polynomial equation of the problem accurately and quickly. Similarly, in [24], Zhou Qiang and others proposed a wolf pack search algorithm based on the leader strategy (LWCA). The researchers in [25] designed an adaptive shrinking grid search chaos wolf optimization algorithm (ASGS-CWOA) using adaptive standard deviation updating to achieve better performance, which included new regeneration rules, a new raid strategy, a new siege strategy and a new siege adaptive step size. Inspired by the good optimization ability of the wolf pack intelligent optimization algorithm and the excellent classification performance of the BP neural network, this article proposes a coal gangue image recognition method based on ASGS-CWOA and the BP neural network.

The remainder of this article is organized as follows. Section 2 presents a description of "*Encircle–City Feature*", a combination of ASGS-CWOA and the BP neural network and an overview of new method. In Section 3, we present comparative experiments to show the effectiveness of the proposed approach. The conclusion is given in Section 4.
