Random following ant colony optimization: Continuous and binary variants for global optimization and feature selection

Zhou, Xinsen; Gui, Wenyong; Heidari, Ali Asghar; Cai, Zhennao; Liang, Guoxi; Chen, Huiling

doi:10.1016/j.asoc.2023.110513

Cited by 36 publications

(3 citation statements)

References 115 publications

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“… Constrained search space : Another limitation arises from the limited search space in which these models operate. The search space, usually between 0 and 1, can lead to a stagnation scenario that hinders the comprehensive exploration of optimal feature subsets [ 23 ]. Integration of metaheuristics : A promising way to overcome these limitations is to integrate metaheuristics into different aspects of the system [ 2 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

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Fractal feature selection model for enhancing high-dimensional biological problems

Alsaeedi,

Al-Mahmood,

Alnaseri

et al. 2024

BMC Bioinformatics

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The integration of biology, computer science, and statistics has given rise to the interdisciplinary field of bioinformatics, which aims to decode biological intricacies. It produces extensive and diverse features, presenting an enormous challenge in classifying bioinformatic problems. Therefore, an intelligent bioinformatics classification system must select the most relevant features to enhance machine learning performance. This paper proposes a feature selection model based on the fractal concept to improve the performance of intelligent systems in classifying high-dimensional biological problems. The proposed fractal feature selection (FFS) model divides features into blocks, measures the similarity between blocks using root mean square error (RMSE), and determines the importance of features based on low RMSE. The proposed FFS is tested and evaluated over ten high-dimensional bioinformatics datasets. The experiment results showed that the model significantly improved machine learning accuracy. The average accuracy rate was 79% with full features in machine learning algorithms, while FFS delivered promising results with an accuracy rate of 94%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fractal feature selection model for enhancing high-dimensional biological problems

Alsaeedi,

Al-Mahmood,

Alnaseri

et al. 2024

BMC Bioinformatics

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show abstract

“…To refine optimization algorithms, researchers consistently engage in innovation by either devising novel algorithms or adapting existing ones to address specific limitations. Their objectives encompass enhancing adaptability, efficiency, robustness, and applicability [41][42][43]. These improvements often target achieving better trade-offs between the exploration and exploitation phases, as well as enhancing the convergence accuracy of the algorithm [44].…”

Section: Introductionmentioning

confidence: 99%

Enhanced slime mould algorithm with backtracking search algorithm: global optimization and feature selection

Wang,

Chen,

Zou

et al. 2024

Preprint

Self Cite

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The Slime Mould Algorithm (SMA), renowned for its swarm-based approach, encounters challenges, particularly in maintaining a balance between exploration and exploitation, leading to a trade-off that impacts its optimization performance. The simple structure and limited hyperparameters of SMA contribute to difficulties in effectively navigating the exploration-exploitation trade-off, with a drawback being its poor ability for exploration. To address these challenges and enhance SMA, this paper introduces BSSMA, an improved variant that incorporates the Backtracking Search Algorithm (BSA). The introduction of the \(phaseratio\) parameter aims to synergize BSA and SMA, capitalizing on the strengths of both algorithms while mitigating their individual drawbacks, including SMA's poor exploration ability. BSA facilitates a thorough exploration, dispersing search agents widely across the solution space, ensuring significant diversity. These search agents then transition to SMA to further refine the search for optimal solutions while addressing SMA's exploration limitations. Evaluating the performance of BSSMA involves comparisons with 12 other meta-heuristic algorithms (MAs) and 10 advanced MAs using the CEC2017 benchmark functions. Experimental results showcase that the enhanced BSSMA outperforms SMA in terms of convergence speed and accuracy, specifically addressing the challenges associated with balancing exploration and exploitation trade-offs, including SMA's poor exploration ability. Additionally, to demonstrate BSSMA's effectiveness in practical engineering applications, a binary version (bBSSMA) is developed for feature selection (FS) using a V-shaped transfer function. Comparative experiments with seven other binary MA variants reveal that bBSSMA selects fewer features, attains higher classification accuracy, and demands less computational time. These results affirm the effectiveness of bBSSMA for practical feature selection applications.

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An enhanced Moth-Flame optimizer with quality enhancement and directional crossover: optimizing classic engineering problems

Yu,

Quan,

Han

et al. 2024

Artif Intell Rev

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Random following ant colony optimization: Continuous and binary variants for global optimization and feature selection

Cited by 36 publications

References 115 publications

Fractal feature selection model for enhancing high-dimensional biological problems

Fractal feature selection model for enhancing high-dimensional biological problems

Enhanced slime mould algorithm with backtracking search algorithm: global optimization and feature selection

An enhanced Moth-Flame optimizer with quality enhancement and directional crossover: optimizing classic engineering problems

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