Aptenodytes Forsteri optimization algorithm based on adaptive perturbation of oscillation and mutation operation for image multi-threshold segmentation

Zhang, Panli; Yang, Jing; Lou, Fanfan; Wang, Jiquan; Sun, Xiaobo

doi:10.1016/j.eswa.2023.120058

Cited by 10 publications

(2 citation statements)

References 60 publications

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“…Therefore, threshold selection and image segmentation involve too many factors, making such heavy tasks and calculations extremely challenging for the human brain [32,33]. Nowadays, with the help of artificial intelligence technology, it is entirely possible to entrust such complex tasks to computers, and they can be performed faster and more accurately than the human brain [6].…”

Section: Related Workmentioning

confidence: 99%

“…Hence, the higher the fitness is, the greater the swarm finds the threshold to segment the target from the background. Common swarm intelligence algorithms include the whale optimization algorithm [1,29], Harris hawks optimization [2], artificial neural networks [3,11,30], deep learning [4,12,21,38], gray wolf optimization [5,39], particle swarm optimization [7,23,40], differential evolution algorithm [9], cuckoo search algorithm [10], ant colony optimization [13,33], genetic algorithm [14,40], artificial bee colony algorithm [15,25], sparrow search algorithm [16], moth swarm algorithm (MSA) [24], emperor penguin optimization (EPO) [26], marine predators algorithm (MPA) [27], salp swarm algorithm (SSA) [27], firefly algorithm (FA) [28], Aptenodytes Forsteri optimization algorithm (AFOA) [32], artificial fish swarm algorithm (AFSA) [36], artificial plant community (APC) [41,42], krill swarm (KS) [43], immune system (IS) [44], naked mole-rat algorithm (NMRA) [45], attention mechanism [46], and so on.…”

Section: Related Workmentioning

confidence: 99%

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Using an Artificial Physarum polycephalum Colony for Threshold Image Segmentation

Cai,

Li,

Zhang

et al. 2023

Applied Sciences

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Traditional artificial intelligence algorithms are prone to falling into local optima when solving threshold segmentation problems. Here, a novel artificial Physarum polycephalum colony algorithm is proposed to help us solve the difficult problem. First, the algorithm methodology of an artificial Physarum polycephalum colony algorithm is described to search for the optimal solutions by expansion and contraction of a lot of artificial hyphae. Different artificial Physarum polycephalum can learn from each other and produce more hyphae in expansion. In contraction, the artificial Physarum polycephalum colony can select the best hyphae with high fitness through a quick sort algorithm, but the other hyphae with low fitness will be absorbed and disappear. Second, a fitness function is modeled based on Kapur’s entropy for the proposed artificial Physarum polycephalum colony algorithm to search for optimal threshold segmentation solutions. Third, a series of benchmark experiments are implemented to test the proposed artificial Physarum polycephalum colony algorithm, and some state-of-the-art approaches are employed for comparison. The experimental results verified that the proposed algorithm can obtain better accuracy and convergence speed, and is not easier to fall into the local optimal solution too early.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Using an Artificial Physarum polycephalum Colony for Threshold Image Segmentation

Cai,

Li,

Zhang

et al. 2023

Applied Sciences

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Multi-threshold image segmentation algorithm based on Aquila optimization

2023

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Ameliorated Fick’s law algorithm based multi-threshold medical image segmentation

Hu,

Zhao,

Hussien

et al. 2024

Artif Intell Rev

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Medical image segmentation is a critical and demanding step in medical image processing, which provides a solid foundation for subsequent medical image data extraction and analysis. Multi-threshold image segmentation, one of the most commonly used and specialized image segmentation techniques, limits its application to medical images because it requires demanding computational performance and is difficult to produce satisfactory segmentation results. To overcome the above problems, an ameliorated Fick's law algorithm (MsFLA) for multi-threshold image segmentation is developed in this paper. First, an optimized sine–cosine strategy is introduced to extend the molecular diffusion process to alleviate the problem of easily falling into local optima, thus improving the convergence accuracy of the Fick's law algorithm (FLA). Secondly, the introduction of local minimal value avoidance enriches the individual molecular information and enhances the local search ability, thus improving computational accuracy. In addition, the optimal neighborhood learning strategy is added to ensure a more careful and reasonable reliance on the optimal solution, thus reducing the chance of convergence of a local solution. The efficient optimization capability of MsFLA is comprehensively validated by comparing MsFLA with the original FLA and other algorithms in 23 classical benchmark functions. Finally, MsFLA is applied to image segmentation of grayscale images of COVID-19 and brain and color images of Lung and Colon cancer histopathology by using Cross entropy to validate its segmentation capability. The experimental results show that the MsFLA obtains the best segmentation results in three medical image cases compared to other comparison algorithms, which indicates that MsFLA can effectively solve the multi-threshold medical image segmentation problem. Graphical abstract

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Aptenodytes Forsteri optimization algorithm based on adaptive perturbation of oscillation and mutation operation for image multi-threshold segmentation

Cited by 10 publications

References 60 publications

Using an Artificial Physarum polycephalum Colony for Threshold Image Segmentation

Using an Artificial Physarum polycephalum Colony for Threshold Image Segmentation

Multi-threshold image segmentation algorithm based on Aquila optimization

Ameliorated Fick’s law algorithm based multi-threshold medical image segmentation

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