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Quantum enhanced colony predation algorithm with episodic memory and energy fatigue for multilevel color image
Tirumalasetti Supraja1, Kankanala Srinivas2
1School of Electronics Engineering, VIT-AP University, Inavolu, Beside AP Secretariat, 522237, Amaravati, AP, India.
Scientific Reports
|July 6, 2026
Summary
An Improved Colony Predation Algorithm (ICPA) enhances multilevel thresholding for complex image segmentation. This new method improves segmentation quality and statistical stability compared to existing optimization algorithms.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Optimization Algorithms
Background:
- Multilevel thresholding is crucial for image segmentation, partitioning images into regions.
- Selecting optimal thresholds, especially for complex color images, remains a significant challenge.
- Existing optimization algorithms often suffer from premature convergence and limited exploration.
Purpose of the Study:
- To propose an Improved Colony Predation Algorithm (ICPA) for enhanced multilevel thresholding.
- To address limitations of existing algorithms such as premature convergence and lack of adaptive exploration.
- To improve the accuracy and stability of image segmentation using optimized threshold values.
Main Methods:
- Introduced quantum tunneling to the encircling phase to prevent local optima.
- Enriched inter-agent communication with an episodic memory pool for successful positions.
- Employed a biologically inspired energy fatigue mechanism for adaptive search behavior.
- Utilized the Minimum Cross Entropy Measure (MCEM) as the objective function for segmentation quality assessment.
Main Results:
- The ICPA demonstrated superior performance in image segmentation compared to WOA, AO, PO, PSO, and EO.
- Achieved better Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM) values.
- Exhibited greater statistical stability across tested threshold levels on the Berkeley Segmentation Dataset (BSDS500).
Conclusions:
- The proposed ICPA effectively overcomes the limitations of traditional optimization algorithms in multilevel thresholding.
- ICPA offers a robust and stable approach for high-quality image segmentation, particularly for complex datasets.
- The enhanced algorithm provides a promising direction for advanced image analysis and computer vision applications.
