Hierarchical Artificial Bee Colony Optimizer with Divide-and-Conquer and Crossover for Multilevel Threshold Image SegmentationReport as inadecuate




Hierarchical Artificial Bee Colony Optimizer with Divide-and-Conquer and Crossover for Multilevel Threshold Image Segmentation - Download this document for free, or read online. Document in PDF available to download.

Discrete Dynamics in Nature and Society - Volume 2014 2014, Article ID 941534, 22 pages -

Research Article

Department of Information Service and Intelligent Control, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China

University of Chinese Academy of Sciences, Beijing 100039, China

Received 11 April 2014; Revised 24 June 2014; Accepted 26 June 2014; Published 2 September 2014

Academic Editor: Zbigniew Leśniak

Copyright © 2014 Maowei He et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization HABC, for multilevel threshold image segmentation, which employs a pool of optimal foraging strategies to extend the classical artificial bee colony framework to a cooperative and hierarchical fashion. In the proposed hierarchical model, the higher-level species incorporates the enhanced information exchange mechanism based on crossover operator to enhance the global search ability between species. In the bottom level, with the divide-and-conquer approach, each subpopulation runs the original ABC method in parallel to part-dimensional optimum, which can be aggregated into a complete solution for the upper level. The experimental results for comparing HABC with several successful EA and SI algorithms on a set of benchmarks demonstrated the effectiveness of the proposed algorithm. Furthermore, we applied the HABC to the multilevel image segmentation problem. Experimental results of the new algorithm on a variety of images demonstrated the performance superiority of the proposed algorithm.





Author: Maowei He, Kunyuan Hu, Yunlong Zhu, Lianbo Ma, Hanning Chen, and Yan Song

Source: https://www.hindawi.com/



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