An Improved Tunicate Swarm Algorithm for Global Optimization and Image Segmentation
This study integrates a tunicate swarm algorithm (TSA) with a local escaping operator (LEO) for overcoming the weaknesses of the original TSA. The LEO strategy in TSA–LEO prevents searching deflation in TSA and improves the convergence rate and local search efficiency of swarm agents. The...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9399412/ |
| _version_ | 1818965899354308608 |
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| author | Essam H. Houssein Bahaa El-Din Helmy Ahmed A. Elngar Diaa Salama Abdelminaam Hassan Shaban |
| author_facet | Essam H. Houssein Bahaa El-Din Helmy Ahmed A. Elngar Diaa Salama Abdelminaam Hassan Shaban |
| author_sort | Essam H. Houssein |
| collection | DOAJ |
| description | This study integrates a tunicate swarm algorithm (TSA) with a local escaping operator (LEO) for overcoming the weaknesses of the original TSA. The LEO strategy in TSA–LEO prevents searching deflation in TSA and improves the convergence rate and local search efficiency of swarm agents. The efficiency of the proposed TSA–LEO was verified on the CEC’2017 test suite, and its performance was compared with seven metaheuristic algorithms (MAs). The comparisons revealed that LEO significantly helps TSA by improving the quality of its solutions and accelerating the convergence rate. TSA–LEO was further tested on a real-world problem, namely, segmentation based on the objective functions of Otsu and Kapur. A set of well-known evaluation metrics was used to validate the performance and segmentation quality of the proposed TSA–LEO. The proposed TSA-LEO outperforms other MA algorithms in terms of fitness, peak signal-to-noise ratio, structural similarity, feature similarity, and segmentation findings. |
| first_indexed | 2024-12-20T13:24:20Z |
| format | Article |
| id | doaj.art-f5d9961bc7a8475581abaf464506186c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T13:24:20Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f5d9961bc7a8475581abaf464506186c2022-12-21T19:39:18ZengIEEEIEEE Access2169-35362021-01-019560665609210.1109/ACCESS.2021.30723369399412An Improved Tunicate Swarm Algorithm for Global Optimization and Image SegmentationEssam H. Houssein0https://orcid.org/0000-0002-8127-7233Bahaa El-Din Helmy1https://orcid.org/0000-0002-1254-0456Ahmed A. Elngar2https://orcid.org/0000-0001-6124-7152Diaa Salama Abdelminaam3https://orcid.org/0000-0002-1544-9906Hassan Shaban4Faculty of Computers and Information, Minia University, Minia, EgyptFaculty of Computers and Artificial Intelligence, Beni-Suef University, Beni Suef, EgyptFaculty of Computers and Artificial Intelligence, Beni-Suef University, Beni Suef, EgyptFaculty of Computers and Artificial Intelligence, Benha University, Benha, EgyptFaculty of Computers and Information, Minia University, Minia, EgyptThis study integrates a tunicate swarm algorithm (TSA) with a local escaping operator (LEO) for overcoming the weaknesses of the original TSA. The LEO strategy in TSA–LEO prevents searching deflation in TSA and improves the convergence rate and local search efficiency of swarm agents. The efficiency of the proposed TSA–LEO was verified on the CEC’2017 test suite, and its performance was compared with seven metaheuristic algorithms (MAs). The comparisons revealed that LEO significantly helps TSA by improving the quality of its solutions and accelerating the convergence rate. TSA–LEO was further tested on a real-world problem, namely, segmentation based on the objective functions of Otsu and Kapur. A set of well-known evaluation metrics was used to validate the performance and segmentation quality of the proposed TSA–LEO. The proposed TSA-LEO outperforms other MA algorithms in terms of fitness, peak signal-to-noise ratio, structural similarity, feature similarity, and segmentation findings.https://ieeexplore.ieee.org/document/9399412/Metaheuristic algorithmstunicate swarm algorithm (TSA)local escaping operator (LEO)multilevel thresholdingimage segmentationKapur’s entropy |
| spellingShingle | Essam H. Houssein Bahaa El-Din Helmy Ahmed A. Elngar Diaa Salama Abdelminaam Hassan Shaban An Improved Tunicate Swarm Algorithm for Global Optimization and Image Segmentation IEEE Access Metaheuristic algorithms tunicate swarm algorithm (TSA) local escaping operator (LEO) multilevel thresholding image segmentation Kapur’s entropy |
| title | An Improved Tunicate Swarm Algorithm for Global Optimization and Image Segmentation |
| title_full | An Improved Tunicate Swarm Algorithm for Global Optimization and Image Segmentation |
| title_fullStr | An Improved Tunicate Swarm Algorithm for Global Optimization and Image Segmentation |
| title_full_unstemmed | An Improved Tunicate Swarm Algorithm for Global Optimization and Image Segmentation |
| title_short | An Improved Tunicate Swarm Algorithm for Global Optimization and Image Segmentation |
| title_sort | improved tunicate swarm algorithm for global optimization and image segmentation |
| topic | Metaheuristic algorithms tunicate swarm algorithm (TSA) local escaping operator (LEO) multilevel thresholding image segmentation Kapur’s entropy |
| url | https://ieeexplore.ieee.org/document/9399412/ |
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