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Archimedes Optimizer: Theory, Analysis, Improvements, and Applications.

Krishna Gopal Dhal1, Swarnajit Ray2, Rebika Rai3

  • 1Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, Midnapore, West Bengal India.

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Summary

The Archimedes Optimization Algorithm (AOA), a physics-inspired method, effectively segments images using Tsallis entropy for better results than t-entropy. This nature-inspired optimization algorithm (NIOA) offers a novel approach to complex problems.

Keywords:
Archimedes optimization algorithmImage segmentationNature-inspired optimization algorithmsOptimizationPhysics inspired optimization algorithmsTsallist-entropy

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Area of Science:

  • Computational intelligence
  • Image processing
  • Optimization algorithms

Background:

  • Classical optimization methods struggle with complex real-world problems.
  • Nature-inspired optimization algorithms (NIOA) offer effective solutions.
  • Physics-based optimization algorithms are a promising subset of NIOA.

Purpose of the Study:

  • To detail the Archimedes Optimization Algorithm (AOA).
  • To evaluate AOA's performance in Multi-Level Thresholding (MLT) image segmentation.
  • To compare AOA with other Physics Inspired Optimization Algorithms (PIOA).

Main Methods:

  • The study details the Archimedes Optimization Algorithm (AOA).
  • AOA was applied to Multi-Level Thresholding (MLT) for image segmentation.
  • Performance was assessed using t-entropy and Tsallis entropy as objective functions.

Main Results:

  • The Archimedes Optimization Algorithm (AOA) demonstrated promising results in image segmentation.
  • AOA using Tsallis entropy outperformed t-entropy for both standard and medical images.
  • AOA showed competitive performance compared to other PIOA.

Conclusions:

  • The Archimedes Optimization Algorithm (AOA) is a potent tool for image segmentation.
  • Tsallis entropy is a more effective objective function than t-entropy when using AOA for MLT.
  • AOA represents a significant advancement in physics-inspired optimization for image analysis.