Enhancing sparrow search algorithm via multi-strategies for continuous optimization problems
作者：
Highlights：
• Proposing a novel uniformity-diversification orientation strategy (UDOS).
• Constructing a novel hazard-aware transfer strategy (HATS) .
• Designing the dynamic evolutionary strategy (DES).
• Improving the global exploration capability and enhancing the convergence accuracy.
• Evaluation of proposed method on 23 benchmark functions, CEC2014, and CEC2017.
• The proposed method outperforms the SSA and other state-of-the-art algorithms, which mentioned in the paper.
摘要
•Proposing a novel uniformity-diversification orientation strategy (UDOS).•Constructing a novel hazard-aware transfer strategy (HATS) .•Designing the dynamic evolutionary strategy (DES).•Improving the global exploration capability and enhancing the convergence accuracy.•Evaluation of proposed method on 23 benchmark functions, CEC2014, and CEC2017.•The proposed method outperforms the SSA and other state-of-the-art algorithms, which mentioned in the paper.
论文关键词：Sparrow search algorithm,Uniformity-diversification orientation strategy,Hazard-aware transfer strategy,Dynamic evolutionary strategy,Density peak clustering
论文评审过程：Received 20 August 2021, Revised 2 December 2021, Accepted 19 December 2021, Available online 31 December 2021, Version of Record 31 December 2021.
论文官网地址：https://doi.org/10.1016/j.ipm.2021.102854
