# An Image Edge Detection Algorithm Based on an Artificial Plant Community
## 2 Relevant Work
The artificial neural network (ANN) and deep learning (DL) methods have undoubtedly been the biggest research topics in this area in recent years. The ANN is a nonlinear and adaptive information processing system composed of a large number of interconnected processing units [15]. Due to the surprising learning performance of the ANN and DL, many scholars have focused on this area, and many new variants have emerged, including the convolutional neural networks (CNNs) [14; 15; 16; 17; 34; 35; 36; 37], the U-net [1], the depth-perceptual network [3], the long short-term memory (LSTM) [5], the edge-net [8], the generative adversarial network [13], the adaptive neuro fuzzy inference system (ANFIS) [14], the Y-net [16], deep learning (DL) [17; 18; 19; 25; 35], machine learning [20], the support vector machine (SVM) [22], the you only look once (YOLO) [35], the word embedding vector [37], and the auto-encoder [38]. The learning characteristics of a neural network can be easily integrated with other algorithms to generate new methods, i.e., [5] combined the LSTM model and the ABC algorithm, [14] incorporated the fuzzy-region and CNN, [18] merged the PSO and DL, and [25] designed a bird swarm optimization algorithm based on deep learning. However, neural networks and deep learning can only learn based on existing data; they cannot judge the correctness of the data and cannot correct the learning results unless they are retrained. In addition, complex neural network calculations are often very time-consuming. The edge detection problem can be seen as a combinatorial optimization problem that is a process of finding the optimal solution to the edge image. Therefore, complex algorithms, such as the CNNs [14; 15; 16; 17; 34; 35; 36; 37] and DL [17; 18; 19; 25; 35], may have a high level of accuracy but take a long time to find a solution. The simulated annealing (SA) algorithm is a random optimization algorithm that can be used to solve the combinatorial optimization problem. It adopts a Monte Carlo iterative solution strategy based on the annealing process of solid materials in physics. In [26], the simulated annealing algorithm was applied to image segmentation using multilevel thresholds. However, the learning ability of the simulated annealing algorithm is not strong. Swarm intelligence algorithms are also random optimization algorithms that simulate the foraging behavior of biological groups in nature, and can easily achieve a balance between the solving time and solving accuracy [39]. The ability or behavior rules of each individual in a swarm intelligence algorithm are very simple, so the implementation of the algorithm is convenient and simple. Common swarm intelligence algorithms include particle swarm optimization (PSO) [18; 40], ant colony optimization (ACO) [19; 21], the artificial bee colony (ABC) [5; 41], the genetic algorithm (GA) [22; 42], the artificial fish swarm algorithm (AFSA) [23], the salp swarm algorithm (SSA) [24], bird swarm optimization (BSO) [25], the naked mole-rat optimizer [26], the firefly algorithm (FA) [30; 41], the grey wolf optimizer (GWO) [32], the sparrow search algorithm (SSA) [43], and the whale optimization algorithm (WOA) [44]. These algorithms provide the great advantages of swarm intelligence to the field of image edge detection and image processing. Both neural network algorithms and swarm intelligence algorithms often use the neural learning function and swarm learning mechanism of organisms to solve problems. In fact, plants, without nerves or brains, have evolved and survived for billions of years on the earth, and they also have own special learning mechanism and evolution strategy [27]. For example, in [28], an artificial slime mold algorithm was used to solve traffic network problems by simulating the natural nerveless slime mold, and in [45], an artificial Physarum swarm algorithm was employed to solve a logistics network problem by imitating the swarm learning mechanism of the nerveless Physarum. Both methods achieved satisfactory solving results.