融合防御式交互机制的人工旅鼠算法及其应用OA

To overcome the weaknesses of the artificial lemming optimization algorithm,such as insufficient convergence accu-racy and susceptibility to local optima in complex optimization problems,this paper proposed an artificial lemming algorithm integrated with a defensive interaction mechanism(IALA).Firstly,it utilized the population's adaptive adjustment factor to dynamically balance the exploration and exploitation capabilities of the algorithm.Secondly,it adopted a random mask dimen-sion learning strategy to enhance problem-solving capabilities in complex search spaces.Furthermore,it introduced a defensive interaction mechanism to strengthen the ability to escape from local optima.Finally,it incorporated a chaotic Gaussian mixture mutation strategy to improve local exploitation accuracy during the later stages of the optimization process.To validate the ef-fectiveness and practicality of the proposed algorithm,this paper conducted comparative experiments against several state-of-the-art algorithms on the CEC2017 and CEC2022 test sets,and applied it to solve three engineering optimization problems.Results demonstrate that the IALA algorithm consistently achieves the top Friedman average rank;compared to the original al-gorithm,its mean error decreases by 28.62％,and the standard deviation decreases by 44.31％on average.In practical engi-neering applications,IALA demonstrates strong applicability and superior performance.