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This research focuses on improving medical image segmentation through a new algorithm inspired by cooperative behavior.

Dispersing-aggregating effect for catch fish optimization: algorithm design, convergence analysis, and application to medical image segmentation

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Algorithm enhances segmentation performance in medical imaging, suggesting improved PSNR and SSIM outcomes.

This research focuses on improving medical image segmentation through a new algorithm inspired by cooperative behavior.
Introduces the Catch Fish Optimization Algorithm with a Dispersing-Aggregating Effect (CFOA-DAE).
Utilizes a hybrid strategy incorporating Gaussian and Linnik distributions for exploration.
Implements an adaptive balance control factor for optimizing search efficiency.
Evaluates the algorithm against 11 classical heuristics and state-of-the-art variants using the CEC 2022 benchmark suite.
CFOA-DAE demonstrates competitive convergence behavior and solution accuracy.
Achieves higher values of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) in segmentation tests.
Improves fitness and lowers Mean Squared Error (MSE) in 75% of cases.

Qu et al. (2026) studied this question.

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View Full Paper

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Authors

Algorithm enhances segmentation performance in medical imaging, suggesting improved PSNR and SSIM outcomes.

This research focuses on improving medical image segmentation through a new algorithm inspired by cooperative behavior.
Introduces the Catch Fish Optimization Algorithm with a Dispersing-Aggregating Effect (CFOA-DAE).
Utilizes a hybrid strategy incorporating Gaussian and Linnik distributions for exploration.
Implements an adaptive balance control factor for optimizing search efficiency.
Evaluates the algorithm against 11 classical heuristics and state-of-the-art variants using the CEC 2022 benchmark suite.
CFOA-DAE demonstrates competitive convergence behavior and solution accuracy.
Achieves higher values of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) in segmentation tests.
Improves fitness and lowers Mean Squared Error (MSE) in 75% of cases.

Qu et al. (2026) studied this question.

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