Abstract
The content of this work is based on the characteristics of standard artificial bee colony(ABC) algorithm with weak local search ability and slow convergence speed. Then, an improved algorithm named KD-ABC is proposed. For improving the diversity and quality of the solution, it changes the generation method of honey source. In the initialization phase, it uses the cluster center generated by the K-MEANS method as the initial honey source instead of the initialization in the standard method. For improving the local optimization ability and the convergence speed without reducing the global search, we proposed a dynamic neighborhood search mechanism based on the number of iterations in terms of ABC search strategy and neighborhood selection stage. In order to find a suitable threshold to divide the grayscale image into blood vessels and background parts, we applied the characteristics of the KD-ABC algorithm to the binary processing stage of the fundus retinal blood vessel image, which lays the foundation for future image recognition.
Highlights
The research direction of swarm intelligence [1] proposed in ‘‘New Generation Artificial Intelligence Development Plan’’ is essentially the expansion and deepening of the new era of artificial intelligence
Artificial bee colony algorithm is a new type of bionic swarm intelligence algorithm proposed by Karaboga in 2005 [2]
This paper proposes an improved artificial bee colony algorithm KD-ABC
Summary
The research direction of swarm intelligence [1] proposed in ‘‘New Generation Artificial Intelligence Development Plan’’ is essentially the expansion and deepening of the new era of artificial intelligence. Artificial bee colony algorithm is a new type of bionic swarm intelligence algorithm proposed by Karaboga in 2005 [2]. The standard artificial bee colony algorithm has some disadvantages of being trapped into a local optimum and having low search efficiency. Aiming at these problems, this paper proposes an improved artificial bee colony algorithm KD-ABC. Our article organization structure is: the first part is the introduction This part mainly introduces the development of swarm intelligence and the importance of retinal blood vessels in clinical diagnosis. This section mainly introduces the research of artificial bee colony algorithm at home and abroad, and briefly introduces our proposed method. The fifth part is the conclusion, which summarizes our proposed method
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