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中南大学学报(英文版)

Journal of Central South University

Vol. 25    No. 1    January 2018

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An enhanced artificial bee colony optimizer and its application to multi-level threshold image segmentation
GAO Yang(高扬)1, LI Xu(李旭)2, DONG Ming(董明)1, LI He-peng(李鹤鹏)3

1. Academy of Information Technology, Northeastern University, Shenyang 110018, China;2. Benedictine University, Lisle, IL, US; 3. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China

Abstract:A modified artificial bee colony optimizer (MABC) is proposed for image segmentation by using a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main idea of MABC is to enrich artificial bee foraging behaviors by combining local search and comprehensive learning using multi-dimensional PSO-based equation. With comprehensive learning, the bees incorporate the information of global best solution into the solution search equation to improve the exploration while the local search enables the bees deeply exploit around the promising area, which provides a proper balance between exploration and exploitation. The experimental results on comparing the MABC to several successful EA and SI algorithms on a set of benchmarks demonstrated the effectiveness of the proposed algorithm. Furthermore, we applied the MABC algorithm to image segmentation problem. Experimental results verify the effectiveness of the proposed algorithm.

 

Key words: artificial bee colony; local search; swarm intelligence; image segmentation

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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