Applications of dynamic adaptive bee colony algorithm in multi-threshold image segmentation
Ye Chen, Xiaoqun Qin
School of Information Science and Engineering, Hunan International Economics University, Changsha 410205, Hunan, China
Artificial bee colony (ABC) is an evolutionary computation method, which is inspired from the specific collaborative social group behaviour among the individual bees in the colony and which is a heuristic optimization algorithm based on population search strategy. This paper has proposed a quick dynamic adaptive bee colony algorithm, which analyses the performances of the artificial bee colony algorithm and it designs a multi-threshold image segmentation method realizing a dynamic adaptive artificial bee colony (DAABC) with multi-threshold OTSU as the fitness function. The main characteristics of this method include: reducing the noise interferences in the multi-threshold image segmentation; effectively narrowing down the search range of the threshold; guaranteeing the quickness of the segmentation speed; determining the search range of the reconnaissance ants with adaptive dynamic control and accelerating the convergence speed of bee colony algorithm. The experimental results demonstrate that the method in this paper is better than the image segmentation method based on particle swarm optimization (PSO) and artificial fish swarm algorithm (AFSA).