An Improved Tunicate Swarm Algorithm for Global Optimization and Image Segmentation

This study integrates a tunicate swarm algorithm (TSA) with local escaping operator (LEO) for overcoming the weaknesses of original TSA. The LEO strategy in TSA–LEO prevents searching deflation TSA and improves convergence rate search efficiency agents. proposed was verified on CEC’2017 test suite, its performance compared seven metaheuristic algorithms (MAs). comparisons revealed that significantly helps by improving quality solutions accelerating rate. further tested real-world problem, namely, segmentation based objective functions Otsu Kapur. A set well-known evaluation metrics used to validate TSA–LEO. TSA-LEO outperforms other MA terms fitness, peak signal-to-noise ratio, structural similarity, feature findings.