Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

Rai, Rebika; Das, Arunita; Dhal, Krishna Gopal

doi:10.1007/s12530-022-09425-5

Cited by 41 publications

(18 citation statements)

References 398 publications

(222 reference statements)

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“…The experimental analysis unquestionably draws attention to the researcher to explore and examine AOA for MLT based image segmentation and broadly in the image processing domain [ 62 ]. This in turn leads to several research directions and challenges and the same has been brought to light below: Two-Dimensional (2D) objective functions [ 7 ] like 2D Cross, 2D-Renyi, 2D Tsallis, 2D Otsu, etc., could be considered and applied over diverse PIOA in the domain of image segmentation making it a challenging task. Other variants of images such as standard grayscale, satellite images, and other medical images such as MRI, CT, histology, etc., could be considered using the numerous PIOA experimented in this paper.…”

Section: Discussionmentioning

confidence: 99%

“…Two-Dimensional (2D) objective functions [ 7 ] like 2D Cross, 2D-Renyi, 2D Tsallis, 2D Otsu, etc., could be considered and applied over diverse PIOA in the domain of image segmentation making it a challenging task.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, this method has strong performance in wood fiber image segmentation thereby having a greater scope in the field of medical image segmentation, forest fire image segmentation, and so on. Rai et al [ 7 ] also performed a comparative study among six NIOA called Archimedes Optimization Algorithm (AROA), Aquila Optimizer (AQO), Arithmetic Optimization Algorithm (AOA), Rat Swarm Optimization Algorithm (RSA), Particle Swarm Optimization (PSO), and Firefly Algorithm (FA) in satellite image MLT domain by considering Masi entropy as an objective function. Experimental results showed that AROA gave better results than other tested NIOA in terms of optimization ability, quality parameters, and execution time.…”

Section: Aoa-based Image Multi-level Thresholdingmentioning

confidence: 99%

“…In light of this, NIOA [ 6 ] and their upgraded variations and subset whose solutions were based on chemical, physical, and biological phenomena in nature showed their usefulness in engineering optimization issues and also in resolving a number of MLT problems [ 7 ]. These nature-inspired solutions happen to be incredibly trendy as they offered to a large extent improved solutions in terms of efficiency and complexity than earlier methods.…”

Section: Introductionmentioning

confidence: 99%

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Archimedes Optimizer: Theory, Analysis, Improvements, and Applications

Dhal

Ray

Rai

et al. 2023

Arch Computat Methods Eng

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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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Aoa-based Image Multi-level Thresholdingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Archimedes Optimizer: Theory, Analysis, Improvements, and Applications

Dhal

Ray

Rai

et al. 2023

Arch Computat Methods Eng

Self Cite

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“…Recently, nature-inspired and heuristic optimization algorithms have been successfully adopted for various applications of image segmentation [15,16]. For example, the Ant lion algorithm, which simulates predators hunting ants, was used to scan over X-ray image for possible degenerated tissues [17].…”

Section: Introductionmentioning

confidence: 99%

Lung X-Ray Image Segmentation Using Heuristic Red Fox Optimization Algorithm

Jaszcz

Połap

Damaševičius

2022

Scientific Programming

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Medical image segmentation identifies an area that should be analyzed later in the processing process, such as for disease recognition and classification. As the image search area is reduced, this action allows for faster computation and analysis. We propose the use of a heuristic red fox heuristic optimization algorithm (RFOA) for medical image segmentation in this paper. The heuristics’ operation was adapted to the analysis of two-dimensional images, with a focus on equation modification and the novel fitness function. The proposed solution analyzes the image by converting the selected pixels to one of two color variants, black or white, based on the threshold value used. Their number is counted, allowing analysis of the chosen threshold. As a result, such analysis results in the automatic selection of the segmentation threshold parameter. Our method propose a new fitness function and the adjustment of RFOA to image analysis. We used a publicly available database of lung X-ray images for evaluation, and based on the results, an accuracy analysis was performed, as well as a discussion of the benefits and drawbacks is presented.

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Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey

Yang,

Gao,

Ding

et al. 2024

WIREs Data Min & Knowl

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This paper reviews the integration of Q‐learning with meta‐heuristic algorithms (QLMA) over the last 20 years, highlighting its success in solving complex optimization problems. We focus on key aspects of QLMA, including parameter adaptation, operator selection, and balancing global exploration with local exploitation. QLMA has become a leading solution in industries like energy, power systems, and engineering, addressing a range of mathematical challenges. Looking forward, we suggest further exploration of meta‐heuristic integration, transfer learning strategies, and techniques to reduce state space.This article is categorized under: Technologies > Computational Intelligence Technologies > Artificial Intelligence

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Nature-inspired optimization algorithms and their significance in multi-thresholding image segmentation: an inclusive review

Cited by 41 publications

References 398 publications

Archimedes Optimizer: Theory, Analysis, Improvements, and Applications

Archimedes Optimizer: Theory, Analysis, Improvements, and Applications

Lung X-Ray Image Segmentation Using Heuristic Red Fox Optimization Algorithm

Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey

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