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GAAOA-Lévy: a hybrid metaheuristic for optimized multilevel thresholding in image segmentation
Eman Mahmoud1, Salem Alkhalaf2, Tomonobu Senjyu3
1Faculty of Science, Aswan University, Aswân, 81528, Egypt.
Scientific Reports
|July 27, 2025
Summary
A new hybrid algorithm, GAAOA-Lévy, enhances multilevel thresholding for image segmentation. This method improves accuracy and efficiency, significantly reducing computational costs in medical and industrial imaging.
Area of Science:
- Computer Vision
- Image Processing
- Optimization Algorithms
Background:
- Multilevel thresholding is crucial for image segmentation in industry and medicine.
- Existing methods face high computational complexity due to exhaustive threshold searching.
Purpose of the Study:
- To address the computational complexity of multilevel thresholding.
- To enhance the efficiency and accuracy of image segmentation techniques.
Main Methods:
- Proposing a hybrid Genetic Algorithm-Archimedes Optimization Algorithm (GAAOA).
- Enhancing the hybrid algorithm with a Lévy flight function (GAAOA-Lévy).
- Integrating GA's crossover mechanism for improved local search.
Main Results:
- GAAOA-Lévy demonstrates superior performance over existing optimization techniques.
- Achieved higher Peak Signal-to-Noise Ratio (PSNR) and faster convergence.
- Showcased significant reduction in computational costs, especially for higher threshold levels.
Conclusions:
- GAAOA-Lévy offers an efficient and accurate solution for multilevel thresholding.
- The algorithm excels in three-level thresholding applications.
- Provides a computationally advantageous approach for complex image segmentation tasks.
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