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 efficiency...

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Veröffentlicht in:	IEEE access Jg. 9; S. 56066 - 56092
Hauptverfasser:	Houssein, Essam H., Helmy, Bahaa El-Din, Elngar, Ahmed A., Abdelminaam, Diaa Salama, Shaban, Hassan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Benchmark testing Convergence Entropy Global optimization Heuristic methods Image segmentation Kapur’s entropy Linear programming local escaping operator (LEO) Metaheuristic algorithms multilevel thresholding Optimization Otsu method Search problems Signal to noise ratio Similarity tunicate swarm algorithm (TSA)
ISSN:	2169-3536, 2169-3536
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Abstract	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.
AbstractList	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.
Author	Elngar, Ahmed A. Helmy, Bahaa El-Din Shaban, Hassan Houssein, Essam H. Abdelminaam, Diaa Salama
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SubjectTerms	Algorithms Benchmark testing Convergence Entropy Global optimization Heuristic methods Image segmentation Kapur’s entropy Linear programming local escaping operator (LEO) Metaheuristic algorithms multilevel thresholding Optimization Otsu method Search problems Signal to noise ratio Similarity tunicate swarm algorithm (TSA)
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Title	An Improved Tunicate Swarm Algorithm for Global Optimization and Image Segmentation
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