【摘要】 针对雪融优化器(snow ablation optimizer, SAO)在求解部分复杂优化问题时存在的寻优效果不稳定、易陷入局部最优等缺陷，提出一种改进算法——改进雪融优化器(improved snow ablation optimizer, ISAO)。该算法基于改进Tent混沌映射提高种群的多样性，引入折射镜像学习改善寻优方向，并结合莱维飞行策略与贪婪策略增强跳出局部最优的能力。同时，选取了5种目前被广泛应用的智能优化算法作为对照组，在10个基准测试函数上和2个多目标优化问题上进行对比实验，其结果显示ISAO相比于SAO具备更强的优化性能。进一步地，将ISAO和SAO分别应用于实际的工程优化问题，其结果验证了ISAO在解决实际工程优化问题上具有显著优势。更多还原

【Abstract】 Aiming at the defects of Snow ablation optimizer(SAO) in solving some complex optimization problems, such as unstable optimization effect and easy to fall into local optimization, an improved algorithm is proposed-Improved Snow ablation optimizer(ISAO). The algorithm is based on improving the Tent chaotic mapping to increase the diversity of the population, introducing the refractive mirror learning strategy to improve the direction of optimization search, and combining the Lévy flight strategy with the greedy strategy to enhance the ability of jumping out of the local optimum. Meanwhile, five widely used intelligent optimization algorithms are selected as the control group, and the comparison experiments are conducted on 10 benchmark test functions and 2 multi-objective optimization problems, and the results show that ISAO has stronger optimization perfor-mance than SAO. Furthermore, ISAO and SAO are applied to real engineering optimization problems, and the results prove that ISAO has significant advantages in solving real engineering optimization problems.更多还原