An Image Edge Detection Algorithm Based on an Artificial Plant Community

Abstract

Image edge detection is a difficult task, because it requires the accurate removal of irrelevant pixels, while retaining important pixels that describe the image’s structural properties. Here, an artificial plant community algorithm is proposed to aid in the solving of the image edge detection problem. First, the image edge detection problem is modeled as an objective function of an artificial plant community searching for water sources and nutrients. After many iterations, the artificial plant community is concentrated in habitable areas that are rich in water sources and nutrients, that is, the image edges, and the nonhabitable zones that are not suitable for living are deserted, that is, the nonedges. Second, an artificial plant community algorithm is designed to solve the objective function by simulating the growth process of a true plant community. The living behavior of the artificial plant community includes three operations: seeding, growing, and fruiting. The individuals in the plant community also correspond to three forms, namely seeds, individuals, and fruit. There are three fitness comparisons in each iteration. The first fitness comparison of each iteration is carried out during the seeding operation. Only the fruit with higher fitness levels in the last iteration can become seeds, while the fruit with low fitness levels die, and some new seeds are randomly generated. The second fitness comparison is implemented in the growing operation. Only the seeds with higher fitness levels can become individuals, but the seeds with lower fitness levels will die; thus, the community size will decrease. The third fitness comparison is in the fruiting operation, where the individual with the greatest fitness can produce an identical fruit through parthenogenesis, and the individuals with higher fitness levels can learn from each other and produce more fruit, so the population size can be restored. Through the continuous cycle of these three operations, the artificial plant community will finally determine the edge pixels and delete the nonedge pixels. Third, the experiment results reveal how the proposed algorithm generates the edge image, and the comparative results demonstrate that the proposed artificial plant community algorithm can effectively solve the image edge detection problems. Finally, this study and some limitations are summarized, and future directions are suggested. The proposed algorithm is expected to act as a new research tool for solving various complex problems.

Using an Artificial Physarum polycephalum Colony for Threshold Image Segmentation

“…Hence, the higher the fitness is, the greater the swarm finds the threshold to segment the target from the background. Common swarm intelligence algorithms include the whale optimization algorithm [1,29], Harris hawks optimization [2], artificial neural networks [3,11,30], deep learning [4,12,21,38], gray wolf optimization [5,39], particle swarm optimization [7,23,40], differential evolution algorithm [9], cuckoo search algorithm [10], ant colony optimization [13,33], genetic algorithm [14,40], artificial bee colony algorithm [15,25], sparrow search algorithm [16], moth swarm algorithm (MSA) [24], emperor penguin optimization (EPO) [26], marine predators algorithm (MPA) [27], salp swarm algorithm (SSA) [27], firefly algorithm (FA) [28], Aptenodytes Forsteri optimization algorithm (AFOA) [32], artificial fish swarm algorithm (AFSA) [36], artificial plant community (APC) [41,42], krill swarm (KS) [43], immune system (IS) [44], naked mole-rat algorithm (NMRA) [45], attention mechanism [46], and so on.…”

A State Transition Diagram and an Artificial Physarum polycephalum Colony Algorithm for the Flexible Job Shop Scheduling Problem with Transportation Constraints

“…It is very important and challenging to design efficient algorithms to address it in large-sized cases, such as simulated annealing (SA) [6] and fuzzy logic (FL) [11]. Among them, swarm intelligence (SI) algorithms have received great attention [23,26], i.e., genetic algorithms (GAs) [2,4,16], particle swarm optimiza-tion (PSO) [6,19], ant colony optimization (ACO) [8], deep learning (DL), artificial neural networks (ANNs) [12,27], artificial bee colony (ABC) [13], adaptive memetic algorithms (AMAs) [14], migrating birds optimization [17], grey wolf optimization (GWO) [20], quantum cat swarm optimization [22], artificial slime mold [28], artificial Physarum swarm [29], coronavirus herd immunity [30], artificial plant community [31,32], whale optimization [33], artificial algae [34], and the Jaya algorithm [35]. However, these swarm intelligence algorithms are also prone to fall into local optimization prematurely, and some scholars have tried to improve algorithm performance using hybrid algorithms [6,36].…”