| 摘 要: 针对基本灰狼优化算法收敛速度慢,易陷入局部搜索的情况,提出一种基于算术运算和透镜成像学习策略的改进灰狼优化算法。该算法在基本灰狼优化算法的基础上,引入算术优化算法的乘除算子,利用带透镜成像的反向学习策略增强最优个体的多样性,增强算法的全局探索能力,提高收敛速度。对比实验结果表明,改进的灰狼优化算法具有收敛速度快、易跳出局部寻优状态,在30个基准测试函数的求解中获得了28个测试函数的最优均值,并且求解质量及普适性均优于最新的几种对比算法。 |
| 关键词: 灰狼优化算法;算术优化算法;透镜成像的反向学习策略 |
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中图分类号: TP391
文献标识码: A
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| 基金项目: 铜仁市科学技术局基础科学研究项目[铜市科研(2022)72号] |
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| An Improved Grey Wolf Optimizer Based on Arithmetic Operations and Lens Imaging Learning Strategy |
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WANG Heng, YANG Ting, GUO Junliang
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(School of In f ormation Engineering, Tongren Polytechnic College, Tongren 554300, China)
wangheng_trzy@foxmail.com; 731922226@qq.com; 286477281@qq.com
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| Abstract: Aiming at the problems that the basic Grey Wolf Optimizer (GWO) converges slowly and is easy to fall into local search, this paper proposes an improved version ALGWO that incorporates arithmetic operations and lens imaging learning strategy. Based on the fundamental principles of GWO, this improved algorithm introduces multiplication and division operators from the Arithmetic Optimization Algorithm (AOA) and utilizes a lens imaging-based opposition learning strategy to enhance the diversity of the optimal individuals. These modifications aim to bolster the algorithm's global exploration capabilities and improve its convergence speed. Comparative experimental results demonstrate that the improved GWO exhibits faster convergence, a stronger ability to escape local optima, and achieves optimal mean values in 28 out of 30 benchmark test functions. Furthermore, it outperforms several state-of-the-art algorithms in terms of solution quality and generality. |
| Keywords: Grey Wolf Optimizer (GWO); Arithmetic Optimization Algorithm (AOA); lens imaging-based opposition learning strategy |
