A crossover strategy integrated dung beetle optimization for global optimization problems and feature selection problems
- 1Department of Artificial Intelligence, Guangzhou Huashang College
- 2School of Computer Science, University of Leeds
Abstract
Dung Beetle Optimization (DBO) is a recent swarm intelligence optimizer inspired by the rolling, dancing, foraging, breeding, and stealing behaviors of dung beetles. Although DBO has shown promising search capability, it may suffer from diversity loss, ineffective inferior-solution utilization, and boundary-induced stagnation when solving complex optimization problems. To address these issues, this paper proposes a Crossover Strategy Integrated Dung Beetle Optimization algorithm, named CSIDBO. The proposed method incorporates three mechanisms: a horizontal crossover strategy for dimension-level information exchange, a precise elimination mechanism for replacing low-quality individuals, and a global-best-guided boundary handling strategy for correcting infeasible solutions. Moreover, a transfer-function-based binary version of CSIDBO is developed for wrapper feature selection. The performance of CSIDBO is evaluated on CEC2017, CEC2020, and CEC2022 benchmark functions and sixteen feature selection datasets. Comparative experiments, ablation studies, convergence curves, exploration–exploitation analysis, runtime comparison, and statistical tests are conducted to provide a comprehensive evaluation. Experimental results show that CSIDBO achieves competitive optimization accuracy and feature selection performance compared with classical algorithms and recent DBO variants. Nevertheless, the results also indicate that CSIDBO is not universally optimal on all functions, and its additional operators introduce extra function evaluations and runtime overhead. These limitations are discussed to provide a balanced assessment of the proposed method.
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BibTeX
@article{chen2026crossover,
title = {{A crossover strategy integrated dung beetle optimization for global optimization problems and feature selection problems}},
author = {Fuqiang Chen and Rui Liu},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-60117-5},
url = {https://doi.org/10.1038/s41598-026-60117-5},
note = {CC BY},
}
RIS
TY - JOUR
TI - A crossover strategy integrated dung beetle optimization for global optimization problems and feature selection problems
AU - Fuqiang Chen
AU - Rui Liu
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-60117-5
UR - https://doi.org/10.1038/s41598-026-60117-5
AB - Dung Beetle Optimization (DBO) is a recent swarm intelligence optimizer inspired by the rolling, dancing, foraging, breeding, and stealing behaviors of dung beetles. Although DBO has shown promising search capability, it may suffer from diversity loss, ineffective inferior-solution utilization, and boundary-induced stagnation when solving complex optimization problems. To address these issues, this paper proposes a Crossover Strategy Integrated Dung Beetle Optimization algorithm, named CSIDBO. The proposed method incorporates three mechanisms: a horizontal crossover strategy for dimension-level information exchange, a precise elimination mechanism for replacing low-quality individuals, and a global-best-guided boundary handling strategy for correcting infeasible solutions. Moreover, a transfer-function-based binary version of CSIDBO is developed for wrapper feature selection. The performance of CSIDBO is evaluated on CEC2017, CEC2020, and CEC2022 benchmark functions and sixteen feature selection datasets. Comparative experiments, ablation studies, convergence curves, exploration–exploitation analysis, runtime comparison, and statistical tests are conducted to provide a comprehensive evaluation. Experimental results show that CSIDBO achieves competitive optimization accuracy and feature selection performance compared with classical algorithms and recent DBO variants. Nevertheless, the results also indicate that CSIDBO is not universally optimal on all functions, and its additional operators introduce extra function evaluations and runtime overhead. These limitations are discussed to provide a balanced assessment of the proposed method.
SN - 2045-2322
ER -