Cloud Model Beetle Swarm Optimization Algorithm with Tent Mapping
GUO Xiaoyu
GAO Ying
WENG Jinta
CAO Can
Abstract：To overcome the shortcomings of the beetle swarm optimization algorithm such as easy falling into local optimum,low optimization precision,and lack of population diversity,a cloud model beetle swarm optimization algorithm with Tent mapping is proposed.Firstly,Tent mapping is used in population initialization to make the initial population evenly distributed in the solution space by generating chaotic random sequences.Then,to make the algorithm have the necessary mutation means,the expectation,entropy,and super entropy of each dimension of the updated population are extracted by the backward cloud generator algorithm and used on the forward cloud generator algorithm to generate a new beetle population.Finally,the position is further updated through the comparison between the new and old population individuals.The simulation experiment uses 20 benchmark functions in-cluding unimodal,multimodal,and fixed-dimension multimodal functions to test the exploration and mining capabilities of the algo-rithm.The results show that the improvement strategy can effectively improve the algorithm's convergence accuracy and population diversity,and make the algorithm have better search performance in different types of function optimization problems.
Keywords：beetle swarm optimization algorithmcloud modelTent mapping
Publication Date：2025-09-20
Online Publishing Date：2025-12-23(First online date of this platform, not the publication date of the document)
Pages：8( 2369-2376 )