Algorithm Selection Method Based on Multi-Objective Hybrid Ant Lion Optimizer

Li Gengsong; Liu Yi; Zheng Qibin; Li Xiang; Liu Kun; Qin Wei; Wang Qiang; Yang Changhong

doi:10.7544/issn1000-1239.202220769

Journal of Computer Research and Development > 2023 > 60(7): 1533-1550. > DOI: 10.7544/issn1000-1239.202220769

Li Gengsong, Liu Yi, Zheng Qibin, Li Xiang, Liu Kun, Qin Wei, Wang Qiang, Yang Changhong. Algorithm Selection Method Based on Multi-Objective Hybrid Ant Lion Optimizer[J]. Journal of Computer Research and Development, 2023, 60(7): 1533-1550. DOI: 10.7544/issn1000-1239.202220769

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Algorithm Selection Method Based on Multi-Objective Hybrid Ant Lion Optimizer

1.
National Innovation Institute of Defense Technology, Beijing 100071
2.
Academy of Military Sciences, Beijing 100091
3.
Tianjin Artificial Intelligence Innovation Center, Tianjin 300457

Funds: This work was supported by the Science and Technology Innovation 2030 Major Project of China (2020AAA0104802), the National Natural Science Foundation of China (91948303), and the National Natural Science Foundation of China for Young Scientists (61802426).

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Author Bio:
Li Gengsong: born in 1999. Master. His main research interests include algorithm selection and big data

Liu Yi: born in 1990. PhD, assistant professor. His main research interests include robot operating system, big data technologies, and evolutionary algorithms.

Zheng Qibin: born in 1990. PhD, assistant professor. His main research interests include data engineering, data mining, and machine learning

Li Xiang: born in 1988. PhD, assistant professor. His main research interest includes big data

Liu Kun: born in 1982. PhD, associate professor. His main research interest includes big data

Qin Wei: born in 1983. Master, assistant professor. His main research interest includes intelligent information system management

Wang Qiang: born in 1972. Master, associate professor. His main research interest includes big data

Yang Changhong: born in 1967. Master, senior engineer. His main research interest includes computer software
Received Date: August 27, 2022
Revised Date: January 31, 2023
Available Online: April 17, 2023

Graphical Abstract

Abstract

Abstract

Algorithm selection refers to selecting an algorithm that satisfies the requirements for a given problem from feasible algorithms, and algorithm selection based on meta-learning is a widely used method, in which the key components are meta-features and meta-learners. However, existing research is difficult to make full use of the complementarity of meta-features and the diversity of meta-learners, which are not conducive to further improving the method performance. To solve the above problems, a selective ensemble algorithm selection method based on multi-objective hybrid ant lion optimizer (SAMO) is proposed. It designs an algorithm selection model, which sets the accuracy and diversity of the ensemble meta-learners as the optimization objectives, introduces meta-feature selection and selective ensemble, and chooses meta-features and heterogeneous meta-learners simultaneously to construct ensemble meta-learners; it proposes a multi-objective hybrid ant lion optimizer to optimize the model, which uses discrete code to select meta-feature subsets and constructs ensemble meta-learners by continuous code, and applies the enhanced walk strategy and the preference elite selection mechanism to improve the optimization performance. We utilize 260 datasets, 150 meta-features, and 9 candidate algorithms to construct classification algorithm selection problems and conduct test experiments, and the parameter sensitivity of the method is analyzed, the multi-objective hybrid ant lion optimizer is compared with four evolutionary algorithms, 8 comparative methods are compared with the proposed method, and the results verify the effectiveness and superiority of the method.
- algorithm selection,
- multi-objective ant lion optimizer,
- meta-feature selection,
- selective ensemble,
- meta-learning,
- classification

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References (48)

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