| WU Jin(吴 进),SU Zhengdong,TIAN Jinhang,WEN Fei,CHEN Wenfeng.[J].高技术通讯(英文),2025,31(1):63~72 |
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| Multi-strategy improved honey badger algorithm based on periodic mutation and t-distribution perturbation |
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| DOI：10. 3772 / j. issn. 1006-6748. 2025. 01. 007 |
| 中文关键词: |
| 英文关键词: periodic mutation, t-distribution, linear decreasing factor, robot path planning |
| 基金项目: |
| Author Name | Affiliation | | WU Jin(吴 进) | (School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P. R. China) | | SU Zhengdong | | | TIAN Jinhang | | | WEN Fei | | | CHEN Wenfeng | |
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| 中文摘要: |
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| 英文摘要: |
| The honey badger algorithm (HBA), as a new swarm intelligence (SI) optimization algorithm,has shown certain effectiveness in its applications. Aiming at the problems of unsatisfactory initial population distribution of HBA, poor ability to avoid local optimum, and slow convergence speed,this paper proposes a multi-strategy improved HBA based on periodical mutation and t-distribution perturbation, called MHBA. Firstly, a good point set population initialization is introduced to get a uniform initial population. Secondly, periodic mutation and t-distribution perturbation are successively used to improve the algorithm’s ability to avoid local optimum. Finally, the density factor is improved for balancing exploration and exploitation. By comparing MHBA with HBA and 7 other SIs on 6 benchmark functions, it is evident that the performance of MHBA is far superior to HBA. In addition, by applying MHBA to robot path planning, MHBA can identify the shortest path more quickly and consistently compared with competitors. |
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