Archimedes Optimizer: Theory, Analysis, Improvements, and Applications

The intricacy of the real-world numerical optimization tribulations has full-fledged and diversely amplified necessitating proficient yet ingenious optimization algorithms. In the domain wherein the classical approaches fall short, the predicament resolving nature-inspired optimization algorithms (N...

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Vydáno v:Archives of computational methods in engineering Ročník 30; číslo 4; s. 2543 - 2578
Hlavní autoři: Dhal, Krishna Gopal, Ray, Swarnajit, Rai, Rebika, Das, Arunita
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.05.2023
Springer Nature B.V
Témata:
Algorithms
Computer engineering
Computer science
Engineering
Entropy
Heuristic
Image segmentation
Mathematical and Computational Engineering
Methods
Optimization
Optimization algorithms
Pathology
Physics
Survey
Survey Article
Image segmentation
t-entropy
Nature-inspired optimization algorithms
Archimedes optimization algorithm
Physics inspired optimization algorithms
Optimization
Tsallis
ISSN:1134-3060, 1886-1784, 1886-1784
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Shrnutí:The intricacy of the real-world numerical optimization tribulations has full-fledged and diversely amplified necessitating proficient yet ingenious optimization algorithms. In the domain wherein the classical approaches fall short, the predicament resolving nature-inspired optimization algorithms (NIOA) tend to hit upon an excellent solution to unbendable optimization problems consuming sensible computation time. Nevertheless, in the last few years approaches anchored in nonlinear physics have been anticipated, announced, and flourished. The process based on non-linear physics modeled in the form of optimization algorithms and as a subset of NIOA, in countless cases, has successfully surpassed the existing optimization methods with their effectual exploration knack thus formulating utterly fresh search practices. Archimedes Optimization Algorithm (AOA) is one of the recent and most promising physics optimization algorithms that use meta-heuristics phenomenon to solve real-world problems by either maximizing or minimizing a variety of measurable variables such as performance, profit, and quality. In this paper, Archimedes Optimization Algorithm (AOA) has been discussed in great detail, and also its performance was examined for Multi-Level Thresholding (MLT) based image segmentation domain by considering t-entropy and Tsallis entropy as objective functions. The experimental results showed that among recent Physics Inspired Optimization Algorithms (PIOA), the Archimedes Optimization Algorithm (AOA) produces very promising outcomes with Tsallis entropy rather than with t-entropy in both color standard images and medical pathology images.
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ISSN:1134-3060
1886-1784
1886-1784
DOI:10.1007/s11831-022-09876-8