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An Improved Tunicate Swarm Algorithm for Global Optimization and Image Segmentation
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DOI：10.1109/ACCESS.2021.3072336.png)
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摘要
En 中文
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.
Keyword:
Optimization
Image segmentation
Search problems
Convergence
Linear programming
Entropy
Benchmark testing
Metaheuristic algorithms
tunicate swarm algorithm (TSA)
local escaping operator (LEO)
multilevel thresholding
image segmentation
Kapur’ s entropy
Otsu method
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm蛾焰优化算法: 一种新的自然启发启发式范式
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