An improved Arctic puffin optimization algorithm with multi-strategys
LIU Tianbao
LI Yang
Abstract：The Arctic puffin optimization algorithm is a metaheuristic optimization algorithm based on the survival and foraging behaviors of the Arctic puffin,which includes two stages:aerial flight and underwater foraging.To address the issues of low convergence accuracy,insufficient population diversity,and the tendency to fall into local optima,an improved Arctic puffin optimization algorithm is proposed.Firstly,a more uniform initial population is obtained through the set of good points.Secondly,using Circle chaotic mapping instead of random numbers helps the algorithm avoid falling into local optimality,thus improving the convergence speed and accuracy of the algorithm.The redesign of behavior transition factor effectively balances global exploration and local exploitation.Finally,the refraction learning strategy is used to increase the possibility of finding the optimal solution.Numerical experimental results show that the improved Arctic puffin optimization algorithm has significant advantages in solving global optimization problems.
Keywords：Arctic puffin optimization algorithmgood point setsCircle chaotic mappingbehavior transition factorrefraction learning
Publication Date：2024-09-15
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 195-204 )