Feature selection based on hybridization of genetic algorithm and competitive swarm optimizer

Ding, Ye; Zhou, Kui; Bi, Weihong

doi:10.1007/s00500-019-04628-6

Cited by 35 publications

(12 citation statements)

References 21 publications

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“…(Gu et al 2018), the CSO is applied to high dimensional FS problem. Recently, a hybrid CSO (Ding et al 2020) algorithm has been proposed to improve the drawbacks of the original CSO with low computational efficiency and avoid falling into local optimum. The CC framework has been applied to differential evolution (DE) as well, namely DECC (Shi et al 2005).…”

Section: Related Workmentioning

confidence: 99%

A self-adaptive level-based learning artificial bee colony algorithm for feature selection on high-dimensional classification

et al. 2022

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Feature selection is an important data preprocessing method in data mining and machine learning, yet it faces the challenge of "curse of dimensionality" when dealing with high-dimensional data. In this paper, a self-adaptive level-based learning artificial bee colony (SLLABC) algorithm is proposed for high-dimensional feature selection problem. The SLLABC algorithm includes three new mechanisms: (1) A novel level-based learning mechanism is introduced to accelerate the convergence of the basic artificial bee colony algorithm, which divides the population into several levels and the individuals on each level learn from the individuals on higher levels, especially, the individuals on the highest level learn from each other. (2) A self-adaptive method is proposed to keep the balance between exploration and exploitation abilities, which takes the diversity of population into account to determine the number of levels. The lower the diversity is, the fewer the levels are divided. (3) A new update mechanism is proposed to reduce the number of selected features. In this mechanism, if the error rate of an offspring is higher than or is equal to that of its parent but selects more features, then the offspring is discarded and the parent is retained, otherwise, the offspring replaces its parent. Further, we discuss and analyze the contribution of these novelties to the diversity of population and the performance of classification. Finally, the results, compared with 8 state-of-the-art algorithms on 12 high-dimensional datasets, confirm the competitive performance of the proposed SLLABC on both classification accuracy and the size of the feature subset.

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Section: Related Workmentioning

confidence: 99%

A self-adaptive level-based learning artificial bee colony algorithm for feature selection on high-dimensional classification

et al. 2022

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“…On the other hand, it uses the niching technique to avoid the algorithm from falling into local optimum. In Ding et al [26], proposed an optimization algorithm that hybridized genetic algorithm and competitive swarm algorithm to solve the feature selection problem. The crossover operator and variation operator from the genetic algorithm were added to the competitive swarm optimization to improve the diversity of new individuals in the algorithm and prevent premature maturation of the population.…”

Section: Genetic Algorithm-based Feature Selection Algorithmsmentioning

confidence: 99%

Two-stage improved Grey Wolf optimization algorithm for feature selection on high-dimensional classification

Shen¹,

Zhang

2021

Complex Intell. Syst.

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In recent years, evolutionary algorithms have shown great advantages in the field of feature selection because of their simplicity and potential global search capability. However, most of the existing feature selection algorithms based on evolutionary computation are wrapper methods, which are computationally expensive, especially for high-dimensional biomedical data. To significantly reduce the computational cost, it is essential to study an effective evaluation method. In this paper, a two-stage improved gray wolf optimization (IGWO) algorithm for feature selection on high-dimensional data is proposed. In the first stage, a multilayer perceptron (MLP) network with group lasso regularization terms is first trained to construct an integer optimization problem using the proposed algorithm for pre-selection of features and optimization of the hidden layer structure. The dataset is compressed using the feature subset obtained in the first stage. In the second stage, a multilayer perceptron network with group lasso regularization terms is retrained using the compressed dataset, and the proposed algorithm is employed to construct the discrete optimization problem for feature selection. Meanwhile, a rapid evaluation strategy is constructed to mitigate the evaluation cost and improve the evaluation efficiency in the feature selection process. The effectiveness of the algorithm was analyzed on ten gene expression datasets. The experimental results show that the proposed algorithm not only removes almost more than 95.7% of the features in all datasets, but also has better classification accuracy on the test set. In addition, the advantages of the proposed algorithm in terms of time consumption, classification accuracy and feature subset size become more and more prominent as the dimensionality of the feature selection problem increases. This indicates that the proposed algorithm is particularly suitable for solving high-dimensional feature selection problems.

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“…PSO is implemented in [6], by Kennedy and Eberhart, emulates bird flocking attitudes to fix optimization issues. In PSO, any solution is considered to be a particle.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

A Novel Method to Denoise Images Based on a Meta-heuristic Algorithm and Pre-learned Dictionary

Alotaibi¹

2021

IJIES

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Noise may affect images in many ways during different processes. Such as during obtaining, distribution, processing, or compressing. The Sparse Representation (SR) algorithm is one of the best strategies for noise reduction. One meta-heuristic algorithm is the Particle Swarm Optimization (PSO). This research demonstrates excellent results in noise reduction in the Fast PSO version while utilizing the SRs as well as meta-heuristic algorithms to gain. This method is known as FPSO-MP and it depends on the Pursuit Algorithm (MP) that matches. In this proposed study, a Dynamic-Multi-Swarm (DMS) method and a pre-learned dictionary (FPSO-MP) approach is presented to reduce the time for the learning dictionary calculations. The output of the denoising algorithm QPSO-MP is dependable on dictionary learning because of the dictionary size or increased number of patches. Similar to this work, a Non-locally Estimated Sparse Coefficient (NESC) is one explanation for the low efficiency of the original algorithm. Compared to the original PSO-MP method, these enhancements have achieved substantial gains in computational efficiency of approximately 92% without sacrosanct image quality. After modification, the proposed FPSO-MP technique is in contrast with the original PSO-MP method. The scientific findings demonstrate that the FPSO-MP algorithm is much more efficient and faster than the original algorithm, without affecting image quality. The proposed method follows the original technique and therefore reduces during run-time. The result of this study demonstrates that the bestdenoised images can always be accessed from the pre-learned dictionary rather than the learning dictionary developed across the noisy image during runtime. We constructed images dataset from the BSD500 collection and performed a statistical test on these images. The actual findings reveal that the suggested method is excellent for noise reduction (noise elimination) as well as highly efficient during runtime. The analytical findings indicate that both quantitative and image performance outcomes are obtained with the proposed FPSO-MP approach during its contradiction with when denoising algorithms.

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Feature selection based on hybridization of genetic algorithm and competitive swarm optimizer

Cited by 35 publications

References 21 publications

A self-adaptive level-based learning artificial bee colony algorithm for feature selection on high-dimensional classification

A self-adaptive level-based learning artificial bee colony algorithm for feature selection on high-dimensional classification

Two-stage improved Grey Wolf optimization algorithm for feature selection on high-dimensional classification

A Novel Method to Denoise Images Based on a Meta-heuristic Algorithm and Pre-learned Dictionary

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