S-shaped versus V-shaped transfer functions for binary Manta ray foraging optimization in feature selection problem

Ghosh, Kushal Kanti; Guha, Ritam; Bera, Suman Kumar; Kumar, Neeraj; Sarkar, Ram

doi:10.1007/s00521-020-05560-9

Cited by 84 publications

(42 citation statements)

References 43 publications

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“…The Manta Ray Foraging Optimization (MRFO) mathematically formulates the foraging strategy of manta rays [25,70]. Three foraging strategies of manta rays are abstracted as optimization rules, i.e., chain foraging, cyclone foraging and somersault foraging.…”

Section: Nine Swarm Intelligence Feature Selection Algorithmsmentioning

confidence: 99%

Zoo: Selecting Transcriptomic and Methylomic Biomarkers by Ensembling Animal-Inspired Swarm Intelligence Feature Selection Algorithms

Han

Huang

Zhou

2021

Genes

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Biological omics data such as transcriptomes and methylomes have the inherent “large p small n” paradigm, i.e., the number of features is much larger than that of the samples. A feature selection (FS) algorithm selects a subset of the transcriptomic or methylomic biomarkers in order to build a better prediction model. The hidden patterns in the FS solution space make it challenging to achieve a feature subset with satisfying prediction performances. Swarm intelligence (SI) algorithms mimic the target searching behaviors of various animals and have demonstrated promising capabilities in selecting features with good machine learning performances. Our study revealed that different SI-based feature selection algorithms contributed complementary searching capabilities in the FS solution space, and their collaboration generated a better feature subset than the individual SI feature selection algorithms. Nine SI-based feature selection algorithms were integrated to vote for the selected features, which were further refined by the dynamic recursive feature elimination framework. In most cases, the proposed Zoo algorithm outperformed the existing feature selection algorithms on transcriptomics and methylomics datasets.

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Section: Nine Swarm Intelligence Feature Selection Algorithmsmentioning

confidence: 99%

Zoo: Selecting Transcriptomic and Methylomic Biomarkers by Ensembling Animal-Inspired Swarm Intelligence Feature Selection Algorithms

Han

Huang

Zhou

2021

Genes

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“…Manta ray foraging optimization (MRFO) is a new swarm intelligence optimization algorithm proposed in 2020. With excellent searchability, fewer parameters, simple model and easily understood, it is better than particle swarm optimization (PSO) [ 11 , 12 ], genetic algorithm (GA) [ 13 , 14 ], Differential Evolution (DE) [ 15 , 16 ], Cuckoo Search (CS) [ 17 ], gravitational search algorithm (GSA) [ 18 ], and ABC [ 19 ] in some function optimization [ 20 ], and it has been successfully applied to solar energy [ 21 , 22 ], ECG [ 23 ], generator [ 24 , 25 ], power system [ 26 ], cogeneration energy system [ 27 ], geophysical inversion problem [ 28 ], directional overcurrent relay [ 29 ], feature selection [ 30 ], hybrid energy system [ 31 ], and sewage treatment [ 32 ]. Although MRFO has good optimization ability, it still has its own defects.…”

Section: Introductionmentioning

confidence: 99%

K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm

Zhu

Xie

Zhou

2022

Computational Intelligence and Neuroscience

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Image segmentation plays an important role in daily life. The traditional K-means image segmentation has the shortcomings of randomness and is easy to fall into local optimum, which greatly reduces the quality of segmentation. To improve these phenomena, a K-means image segmentation method based on improved manta ray foraging optimization (IMRFO) is proposed. IMRFO uses Lévy flight to improve the flexibility of individual manta rays and then puts forward a random walk learning that prevents the algorithm from falling into the local optimal state. Finally, the learning idea of particle swarm optimization is introduced to enhance the convergence accuracy of the algorithm, which effectively improves the global and local optimization ability of the algorithm simultaneously. With the probability that K-means will fall into local optimum reducing, the optimized K-means hold stronger stability. In the 12 standard test functions, 7 basic algorithms and 4 variant algorithms are compared with IMRFO. The results of the optimization index and statistical test show that IMRFO has better optimization ability. Eight underwater images were selected for the experiment and compared with 11 algorithms. The results show that PSNR, SSIM, and FSIM of IMRFO in each image are better. Meanwhile, the optimized K-means image segmentation performance is better.

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“…Therefore, physicists have sought to formulate approaches to solve the feature selection (FS) problem and provide solutions more efficiently than conventional techniques. This problem is considered one of the most recent problems faced by the new technology due to the size of the available information and data [24][25][26]. The use of metaheuristic algorithms is one such approach.…”

Section: Introductionmentioning

confidence: 99%

Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model

et al. 2021

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Feature selection is a well-known prepossessing procedure, and it is considered a challenging problem in many domains, such as data mining, text mining, medicine, biology, public health, image processing, data clustering, and others. This paper proposes a novel feature selection method, called AOAGA, using an improved metaheuristic optimization method that combines the conventional Arithmetic Optimization Algorithm (AOA) with the Genetic Algorithm (GA) operators. The AOA is a recently proposed optimizer; it has been employed to solve several benchmark and engineering problems and has shown a promising performance. The main aim behind the modification of the AOA is to enhance its search strategies. The conventional version suffers from weaknesses, the local search strategy, and the trade-off between the search strategies. Therefore, the operators of the GA can overcome the shortcomings of the conventional AOA. The proposed AOAGA was evaluated with several well-known benchmark datasets, using several standard evaluation criteria, namely accuracy, number of selected features, and fitness function. Finally, the results were compared with the state-of-the-art techniques to prove the performance of the proposed AOAGA method. Moreover, to further assess the performance of the proposed AOAGA method, two real-world problems containing gene datasets were used. The findings of this paper illustrated that the proposed AOAGA method finds new best solutions for several test cases, and it got promising results compared to other comparative methods published in the literature.

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S-shaped versus V-shaped transfer functions for binary Manta ray foraging optimization in feature selection problem

Cited by 84 publications

References 43 publications

Zoo: Selecting Transcriptomic and Methylomic Biomarkers by Ensembling Animal-Inspired Swarm Intelligence Feature Selection Algorithms

Zoo: Selecting Transcriptomic and Methylomic Biomarkers by Ensembling Animal-Inspired Swarm Intelligence Feature Selection Algorithms

K-Means Segmentation of Underwater Image Based on Improved Manta Ray Algorithm

Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model

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