A comparative study of nature inspired optimization algorithms on multilevel thresholding image segmentation

Ameur, Mustapha; Habba, Maryam; Jabrane, Younes

doi:10.1007/s11042-019-08133-8

Cited by 15 publications

(3 citation statements)

References 12 publications

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“…www.nature.com/scientificreports/ Here, X represents a set of current candidate solutions, which are randomly generated by Eq. (2). X i denotes the decision value (position) of the i th solution, N is the total number of candidate solutions (population), and Dim represents the size of the problem dimension.…”

Section: Initialization Processmentioning

confidence: 99%

Enhanced Aquila optimizer based on tent chaotic mapping and new rules

Fu,

Liu,

et al. 2024

Sci Rep

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Metaheuristic algorithms, widely applied across various domains due to their simplicity and strong optimization capabilities, play a crucial role in problem-solving. While the Aquila Optimizer is recognized for its effectiveness, it often exhibits slow convergence rates and susceptibility to local optima in certain scenarios. To address these concerns, this paper introduces an enhanced version, termed Tent-enhanced Aquila Optimizer (TEAO). TEAO incorporates the Tent chaotic map to initialize the Aquila population, promoting a more uniform distribution within the solution space. To balance exploration and exploitation, novel formulas are proposed, accelerating convergence while ensuring precision. The effectiveness of the TEAO algorithm is validated through a comprehensive comparison with 14 state-of-the-art algorithms using 23 classical benchmark test functions. Additionally, to assess the practical feasibility of the approach, TEAO is applied to six constrained engineering problems and benchmarked against the performance of the same 14 algorithms. All experimental results consistently demonstrate that TEAO outperforms other advanced algorithms in terms of solution quality and stability, establishing it as a more competitive choice for optimization tasks.

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Section: Initialization Processmentioning

confidence: 99%

Enhanced Aquila optimizer based on tent chaotic mapping and new rules

Fu,

Liu,

et al. 2024

Sci Rep

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“…They are particularly effective in optimization tasks because of their robust search mechanisms. In [ 33 ], the authors adopted a unique optimization approach based on GA principles. The method was implemented for multilevel thresholding image segmentation, which is a task that involves the division of an image into various regions.…”

Section: Literature Reviewmentioning

confidence: 99%

Medical Image Segmentation Using Automatic Optimized U-Net Architecture Based on Genetic Algorithm

Khouy,

Jabrane,

Ameur

et al. 2023

JPM

Self Cite

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Image segmentation is a crucial aspect of clinical decision making in medicine, and as such, it has greatly enhanced the sustainability of medical care. Consequently, biomedical image segmentation has become a prominent research area in the field of computer vision. With the advent of deep learning, many manual design-based methods have been proposed and have shown promising results in achieving state-of-the-art performance in biomedical image segmentation. However, these methods often require significant expert knowledge and have an enormous number of parameters, necessitating substantial computational resources. Thus, this paper proposes a new approach called GA-UNet, which employs genetic algorithms to automatically design a U-shape convolution neural network with good performance while minimizing the complexity of its architecture-based parameters, thereby addressing the above challenges. The proposed GA-UNet is evaluated on three datasets: lung image segmentation, cell nuclei segmentation in microscope images (DSB 2018), and liver image segmentation. Interestingly, our experimental results demonstrate that the proposed method achieves competitive performance with a smaller architecture and fewer parameters than the original U-Net model. It achieves an accuracy of 98.78% for lung image segmentation, 95.96% for cell nuclei segmentation in microscope images (DSB 2018), and 98.58% for liver image segmentation by using merely 0.24%, 0.48%, and 0.67% of the number of parameters in the original U-Net architecture for the lung image segmentation dataset, the DSB 2018 dataset, and the liver image segmentation dataset, respectively. This reduction in complexity makes our proposed approach, GA-UNet, a more viable option for deployment in resource-limited environments or real-world implementations that demand more efficient and faster inference times.

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“…Nonetheless, some prime challenges that tend to swivel around EC which demands to be addressed are: Lack of accepted benchmark problems; Lack of standard algorithms and implementations, Lack of mechanism for fine parameter control and tuning, Lack of methods to measure performance etc., Presently substantial amount of work has been carried forward concentrating typically on the procedures of natural selection thus developing new algorithms inspired by human. However, human behavior and evolution give power to human to familiarize with their atmospheres at rates that surpass that of other nature based evolution namely swarm, bio-inspired, plant-based or physics-chemistry based thus instigation yet other compartment of Nature-Inspired Optimization Algorithm (NIOA) [ 11 – 14 ] i.e. Human-Inspired Optimization Algorithms (HIOAs).…”

Section: Introductionmentioning

confidence: 99%

Human-Inspired Optimization Algorithms: Theoretical Foundations, Algorithms, Open-Research Issues and Application for Multi-Level Thresholding

Rai

Das

Ray

et al. 2022

Arch Computat Methods Eng

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Humans take immense pride in their ability to be unpredictably intelligent and despite huge advances in science over the past century; our understanding about human brain is still far from complete. In general, human being acquire the high echelon of intelligence with the ability to understand, reason, recognize, learn, innovate, retain information, make decision, communicate and further solve problem. Thereby, integrating the intelligence of human to develop the optimization technique using the human problem-solving ability would definitely take the scenario to next level thus promising an affluent solution to the real world optimization issues. However, human behavior and evolution empowers human to progress or acclimatize with their environments at rates that exceed that of other nature based evolution namely swarm, bio-inspired, plant-based or physics-chemistry based thus commencing yet additional detachment of Nature-Inspired Optimization Algorithm (NIOA) i.e. Human-Inspired Optimization Algorithms ( HIOAs ). Announcing new meta-heuristic optimization algorithms are at all times a welcome step in the research field provided it intends to address problems effectively and quickly. The family of HIOA is expanding rapidly making it difficult for the researcher to select the appropriate HIOA; moreover, in order to map the problems alongside HIOA, it requires proper understanding of the theoretical fundamental, major rules governing HIOAs as well as common structure of HIOAs. Common challenges and open research issues are yet another important concern in HIOA that needs to be addressed carefully. With this in mind, our work distinguishes HIOAs on the basis of a range of criteria and discusses the building blocks of various algorithms to achieve aforementioned objectives. Further, this paper intends to deliver an acquainted survey and analysis associated with modern compartment of NIOA engineered upon the perception of human behavior and intelligence i.e. Human-Inspired Optimization Algorithms ( HIOAs ) stressing on its theoretical foundations, applications, open research issues and their implications on color satellite image segmentation to further develop Multi-Level Thresholding (MLT) models utilizing Tsallis and t-entropy as objective functions to judge their efficacy.

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A comparative study of nature inspired optimization algorithms on multilevel thresholding image segmentation

Cited by 15 publications

References 12 publications

Enhanced Aquila optimizer based on tent chaotic mapping and new rules

Enhanced Aquila optimizer based on tent chaotic mapping and new rules

Medical Image Segmentation Using Automatic Optimized U-Net Architecture Based on Genetic Algorithm

Human-Inspired Optimization Algorithms: Theoretical Foundations, Algorithms, Open-Research Issues and Application for Multi-Level Thresholding

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