Application of Chaos Cuckoo Search Algorithm in computer vision technology

Fan, Jiayi; Xu, Wenjing; Huang, Yi; Samuel, R. Dinesh Jackson

doi:10.1007/s00500-021-05950-8

Cited by 15 publications

(4 citation statements)

References 30 publications

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“…At the same time, the cuckoo algorithm and its improved algorithm also have a very broad application, such as PID controller design [ 27 ], grayscale image enhancement [ 28 ], cryptanalysis of Vigenere ciphers [ 29 ], data privacy protection [ 30 ], image segmentation [ 31 ], spam detection [ 32 ], the design of multidocument summarization extractor [ 33 ], and robot path planning [ 34 , 35 ].…”

Section: Related Workmentioning

confidence: 99%

Cuckoo Algorithm Based on Global Feedback

Liu

Lai

et al. 2023

Computational Intelligence and Neuroscience

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This article proposes a cuckoo algorithm (GFCS) based on the global feedback strategy and innovatively introduces a “re-fly” mechanism. In GFCS, the process of the algorithm is adjusted and controlled by a dynamic global variable, and the dynamic global parameter also serves as an indicator of whether the algorithm has fallen into a local optimum. According to the change of the global optimum value of the algorithm in each round, the dynamic global variable value is adjusted to optimize the algorithm. In addition, we set new formulas for the other main parameters, which are also adjusted by the dynamic global variable as the algorithm progresses. When the algorithm converges prematurely and falls into a local optimum, the current optimum is retained, and the algorithm is initialized and re-executed to find a better value. We define the previous process as “re-fly.” To verify the effectiveness of GFCS, we conducted extensive experiments on the CEC2013 test suite. The experimental results show that the GFCS algorithm has better performance compared to other algorithms when considering the quality of the obtained solution.

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Section: Related Workmentioning

confidence: 99%

Cuckoo Algorithm Based on Global Feedback

Liu

Lai

et al. 2023

Computational Intelligence and Neuroscience

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“…Cuckoo search (CS) algorithm is a novel intelligent optimization algorithm proposed by Yang [13], which is inspired by the parasitic breeding habit of cuckoos, and it adopts the Lévy ight mode to update the individuals, which can effectively jump out of the local optimum. With the advantages of few parameters, simple operation, fast convergence and strong global optimization ability, the CS algorithm has been successfully applied to a variety of elds, such as multi-objective optimization [14,15], image processing [16,17], resource allocation [18,19], control problems [20,21], and computer vision [22].…”

Section: Introductionmentioning

confidence: 99%

Joint inversion for magnetotelluric data based on cuckoo search algorithm and least squares method

Li,

Zhang,

Ding

et al. 2024

Preprint

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The main drawback of using traditional linear or quasi-linear methods for magnetotelluric (MT) data inversion is its strong dependence on the initial model, which leads to its tendency to fall into a local optimum. However, the lack of sufficient a priori information makes it difficult to obtain suitable initial models in most geophysical inversions. To solve this problem, a joint approach (CS-MLS) based on the cuckoo search (CS) algorithm and the moving least squares (MLS) method is proposed to perform MT data inversion. The CS algorithm has a good global search performance without being sensitive to the initial model, which is used for the initial search of the stratum model parameter space. The search results are then used as the initial model to explore the quasi-linear inversion based on MLS. The synthetic and field data experiments show that the reliable initial model provided by the CS algorithm has greatly refined the results of the MLS inversion. The joint CS-MLS algorithm has great inversion performance, with significant improvement in inversion efficiency and accuracy, and is especially suitable for MT data inversion without initial information.

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“…The CS algorithm has been used for capacitated vehicle routing [12], optimal tuning of PID controllers [13], optimal heat exchanger network synthesis [14], imaging systems [15,16], selection of elective subjects in schools [17], in computer vision technology [18], multi-threshold image segmentation [19], and the COVID-19 problem [20], among others.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study between Type-1 and Interval Type-2 Fuzzy Systems in Parameter Adaptation for the Cuckoo Search Algorithm

2022

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The objective of this work is focused on improving the optimization ability of the cuckoo search algorithm (CS), and, for this reason, a comparison is made between type-1 and interval type-2 fuzzy logic to look for more promising results in the cuckoo search algorithm (CS), and to help performance, we dynamically adjust the alpha parameter. The idea is to enable CS in leaving the local optima, and then be able to reach the global optima. Currently, there are good results in improving the optimization of algorithms through intelligent fuzzy logic computing after finding the best adjustment parameters. The approach is based on finding the ideal rules with their respective linguistic variables to represent the real world as is perceived by humans. The membership functions that the fuzzy system uses are symmetrically defined for reducing the search space, and this symmetry is what makes the algorithm efficient. We plan to test the proposal in future works in the optimal design of control systems. In the present study, we use five benchmark mathematical functions with variation in the number of dimensions to validate the approach and perform the comparison of interval type-2 and type-1 fuzzy systems in parameter adaptation. For the dynamic adjustment of the parameters, we select the alpha parameter, and the values of Pa and Beta are defined based on the analysis of their behavior in previous works.

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Application of Chaos Cuckoo Search Algorithm in computer vision technology

Cited by 15 publications

References 30 publications

Cuckoo Algorithm Based on Global Feedback

Cuckoo Algorithm Based on Global Feedback

Joint inversion for magnetotelluric data based on cuckoo search algorithm and least squares method

Comparative Study between Type-1 and Interval Type-2 Fuzzy Systems in Parameter Adaptation for the Cuckoo Search Algorithm

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