An Improved Northern Goshawk Optimization Algorithm for Feature Selection

Xie, Rongxiang; Li, Shaobo; Wu, Fengbin

doi:10.1007/s42235-024-00515-5

J Bionic Eng

2024

DOI: 10.1007/s42235-024-00515-5

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An Improved Northern Goshawk Optimization Algorithm for Feature Selection

Rongxiang Xie,

Shaobo Li,

Fengbin Wu

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2024

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Cited by 2 publications

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An intensified northern goshawk optimization algorithm for solving optimization problems

Wang

2024

Eng. Res. Express

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To address the shortcomings of the Northern Goshawk Optimization (NGO) algorithm, such as insufficient search capability, slow convergence speed, and easy to fall into local optimality in the late iteration, an Improved Northern Goshawk Optimization (INGO) algorithm is proposed. INGO algorithm uses the good point set strategy to generate balanced distributed solutions in the search space, which improves the quality of the initial population. The axial mapping differential variation mechanism is introduced to generate candidate solutions, which expands the discovery field of the best solutions and improves the ability of the algorithm to escape from local extremes, and further enhances the algorithm's global space exploration ability. Through the Gaussian differential variation strategy, the population diversity is enhanced, the convergence process of the algorithm is accelerated, and the local space search ability of the algorithm is improved. To evaluate the performance of the INGO algorithm, the INGO algorithm is used with seven newly published optimization algorithms to solve a total of 53 test functions in two benchmark function sets, CEC2014 and CEC2017. The numerical results show that INGO algorithm has significantly better performance than the seven competing algorithms in terms of convergence speed, search accuracy, and stability.

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An intensified northern goshawk optimization algorithm for solving optimization problems

Wang

2024

Eng. Res. Express

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FTDZOA: An Efficient and Robust FS Method with Multi-Strategy Assistance

Chen,

Ye,

et al. 2024

Biomimetics

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Feature selection (FS) is a pivotal technique in big data analytics, aimed at mitigating redundant information within datasets and optimizing computational resource utilization. This study introduces an enhanced zebra optimization algorithm (ZOA), termed FTDZOA, for superior feature dimensionality reduction. To address the challenges of ZOA, such as susceptibility to local optimal feature subsets, limited global search capabilities, and sluggish convergence when tackling FS problems, three strategies are integrated into the original ZOA to bolster its FS performance. Firstly, a fractional order search strategy is incorporated to preserve information from the preceding generations, thereby enhancing ZOA’s exploitation capabilities. Secondly, a triple mean point guidance strategy is introduced, amalgamating information from the global optimal point, a random point, and the current point to effectively augment ZOA’s exploration prowess. Lastly, the exploration capacity of ZOA is further elevated through the introduction of a differential strategy, which integrates information disparities among different individuals. Subsequently, the FTDZOA-based FS method was applied to solve 23 FS problems spanning low, medium, and high dimensions. A comparative analysis with nine advanced FS methods revealed that FTDZOA achieved higher classification accuracy on over 90% of the datasets and secured a winning rate exceeding 83% in terms of execution time. These findings confirm that FTDZOA is a reliable, high-performance, practical, and robust FS method.

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An Improved Northern Goshawk Optimization Algorithm for Feature Selection

Cited by 2 publications

References 52 publications

An intensified northern goshawk optimization algorithm for solving optimization problems

An intensified northern goshawk optimization algorithm for solving optimization problems

FTDZOA: An Efficient and Robust FS Method with Multi-Strategy Assistance

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