Multi-strategy improved honey badger algorithm based on periodic mutation and t-distribution perturbation  

作　　者：WU Jin SU Zhengdong TIAN Jinhang WEN Fei CHEN Wenfeng 吴进

机构地区：[1]School of Electronic Engineering,Xi’an University of Posts and Telecommunications,Xi’an 710121,P.R.China

出　　处：《High Technology Letters》2025年第1期63-72,共10页高技术通讯(英文版)

基　　金：Supported by the National Key Research and Development Program of China(No.2022ZD0119001)。

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.

关 键 词：periodic mutation T-DISTRIBUTION linear decreasing factor robot path planning 

分 类 号：O17[理学—数学]