Research on Squirrel Optimization Algorithm Integrating Adaptive t Distribution and Random Walk Strategy
ZHANG Lian
JIA Hao
ZHANG Shangde
ZHAO Mengqi
ZHAO Na
HUANG Wei
Abstract：To solve the problem of limited searching ability,easy falling into local optimum,and huge loss of population vari-ety,a novel squirrel optimization algorithm(TRWSSA)is developed,which combines adaptive t-distribution with random walk strategy.For population initialization,the method employs a refraction reverse learning technique,which increases the population's total variety.The chance of the method falling into a local optimum is lowered and the global optimization ability is boosted by incor-porating a non-linear search factor and adding an adaptive t-distribution perturbation site to each squirrel location update.A ran-dom walk approach is introduced to the algorithm's last position update to update the ideal squirrel location,which improves the al-gorithm's convergence accuracy and speed.The experimental findings and analysis reveal that TRWSSA has a considerable improve-ment in convergence speed and accuracy,and it can better tackle the problem of insufficient optimization,when compared to other intelligent algorithms and improved algorithms.
Keywords：intelligent optimization algorithmsquirrel algorithmalgorithm improvementintegration strategyrefraction reverse learningadaptive t distributionrandom walkbenchmark function
Publication Date：2024-08-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 2343-2347,2410 )