Image segmentation of Leaf Spot Diseases on Maize using multi-stage Cauchy-enabled grey wolf algorithm

Yu, Honggang; Song, Jiuman; Chen, Chengcheng; Heidari, Ali Asghar; Liu, Jiawen; Chen, Huiling; Zaguia, Atef; Mafarja, Majdi

doi:10.1016/j.engappai.2021.104653

Cited by 88 publications

(35 citation statements)

References 139 publications

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“…Image segmentation has been applied to solve many practical problems, such as leaf spot disease image segmentation [ 40 ], breast cancer image segmentation [ 41 , 42 ], and lupus nephritis image segmentation. Today, many COVID-19 X-ray image segmentation methods have been proposed by researchers.…”

Section: Related Workmentioning

confidence: 99%

Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation

Zhao

et al. 2022

Computers in Biology and Medicine

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160

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Section: Related Workmentioning

confidence: 99%

Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation

Zhao

et al. 2022

Computers in Biology and Medicine

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160

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“…Randomized balanced grey wolf optimizer (RBGWO), which improves the overall efficiency of the search process by establishing a balance between its exploitation and exploration capability incorporating three successive enhancement strategies equipped with a social hierarchy mechanism and random walk with student's t-distributed random numbers [ 41 ]. By dividing the search process into three stages and using different population updating strategies at each stage, an improved GWO called multistage grey wolf optimizer (MGWO) is proposed; the MGWO is improved while maintaining a certain convergence speed [ 42 ].…”

Section: Related Workmentioning

confidence: 99%

Equalized Grey Wolf Optimizer with Refraction Opposite Learning

Sun

Feng

Chen

et al. 2022

Computational Intelligence and Neuroscience

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Grey wolf optimizer (GWO) is a global search algorithm based on grey wolf hunting activity. However, the traditional GWO is prone to fall into local optimum, affecting the performance of the algorithm. Therefore, to solve this problem, an equalized grey wolf optimizer with refraction opposite learning (REGWO) is proposed in this study. In REGWO, the issue about the low swarm population variety of GWO in the late iteration is well overcome by the opposing learning of refraction. In addition, the equilibrium pool strategy reduces the likelihood of wolves going to the local extremum. To investigate the effectiveness of REGWO, it is evaluated on 21 widely used benchmark functions and IEEE CEC 2019 test functions. Experimental results show/ that REGWO performs better than the other competitors on most benchmarks.

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“…Several swarm intelligence optimization techniques have appeared successively in recent years, such as slime mould algorithm (SMA) [ 35 ], Harris hawks optimization (HHO) [ 61 ], hunger games search (HGS) [ 62 ], Runge Kutta optimizer (RUN) [ 63 ], colony predation algorithm (CPA) [ 64 ], and weighted mean of vectors (INFO) [ 65 ]. Due to the simplicity and efficiency of swarm intelligence algorithms, they have been widely used in many different fields, such as image segmentation [ 66 , 67 ], the traveling salesman problem [ 68 ], feature selection [ 69 , 70 ], practical engineering problems [ 71 , 72 ], fault diagnosis [ 73 ], scheduling problems [ 74 , 75 , 76 ], multi-objective problems [ 77 , 78 ], medical diagnosis [ 79 , 80 ], economic emission dispatch problems [ 81 ], robust optimization [ 82 , 83 ], solar cell parameter identification [ 84 ], and optimization of machine learning models [ 85 ]. Among them, SMA is a new bionic stochastic optimization problem, simulating slime mold behavior and morphological changes during foraging.…”

Section: The Proposed Ismamentioning

confidence: 99%

Mutational Slime Mould Algorithm for Gene Selection

et al. 2022

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A large volume of high-dimensional genetic data has been produced in modern medicine and biology fields. Data-driven decision-making is particularly crucial to clinical practice and relevant procedures. However, high-dimensional data in these fields increase the processing complexity and scale. Identifying representative genes and reducing the data’s dimensions is often challenging. The purpose of gene selection is to eliminate irrelevant or redundant features to reduce the computational cost and improve classification accuracy. The wrapper gene selection model is based on a feature set, which can reduce the number of features and improve classification accuracy. This paper proposes a wrapper gene selection method based on the slime mould algorithm (SMA) to solve this problem. SMA is a new algorithm with a lot of application space in the feature selection field. This paper improves the original SMA by combining the Cauchy mutation mechanism with the crossover mutation strategy based on differential evolution (DE). Then, the transfer function converts the continuous optimizer into a binary version to solve the gene selection problem. Firstly, the continuous version of the method, ISMA, is tested on 33 classical continuous optimization problems. Then, the effect of the discrete version, or BISMA, was thoroughly studied by comparing it with other gene selection methods on 14 gene expression datasets. Experimental results show that the continuous version of the algorithm achieves an optimal balance between local exploitation and global search capabilities, and the discrete version of the algorithm has the highest accuracy when selecting the least number of genes.

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Image segmentation of Leaf Spot Diseases on Maize using multi-stage Cauchy-enabled grey wolf algorithm

Cited by 88 publications

References 139 publications

Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation

Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation

Equalized Grey Wolf Optimizer with Refraction Opposite Learning

Mutational Slime Mould Algorithm for Gene Selection

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