An enhanced artificial bee colony optimizer and its application to multi-level threshold image segmentation
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 l...
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| Published in | Journal of Central South University Vol. 25; no. 1; pp. 107 - 120 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Changsha
Central South University
01.01.2018
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2095-2899 2227-5223 |
| DOI | 10.1007/s11771-018-3721-z |
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| 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. |
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| AbstractList | 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. |
| Author | Gao, Yang Li, He-peng Li, Xu Dong, Ming |
| Author_xml | – sequence: 1 givenname: Yang orcidid: 0000-0002-1858-6324 surname: Gao fullname: Gao, Yang email: gaoy@mail.neu.edu.cn organization: Academy of Information Technology, Northeastern University – sequence: 2 givenname: Xu surname: Li fullname: Li, Xu organization: Benedictine University – sequence: 3 givenname: Ming surname: Dong fullname: Dong, Ming organization: Academy of Information Technology, Northeastern University – sequence: 4 givenname: He-peng surname: Li fullname: Li, He-peng organization: Shenyang Institute of Automation, Chinese Academy of Sciences |
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| Copyright | Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018 Copyright Springer Science & Business Media 2018 |
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| Keywords | artificial bee colony 人工蜂群算法 image segmentation 局部搜索 图像分割 local search swarm intelligence 群体智能 |
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| SubjectTerms | Algorithms Engineering Exploitation Exploration Forage Image enhancement Image segmentation Metallic Materials Searching Swarm intelligence |
| Title | An enhanced artificial bee colony optimizer and its application to multi-level threshold image segmentation |
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