| 摘 要: 为提高灰狼优化算法种群多样性和搜索解的质量，提出一种基于Tent混沌函数与反向学习机制的非线性灰狼优化算法。采用Tent混沌函数和反向学习机制进行种群个体初始化，使得初始种群个体分布均匀及多样性增强；引入一种非线性收敛因子控制策略，平衡其全局搜索能力和局部搜索能力；引入动态权重策略以提升灰狼优化算法的收敛速度和收敛能力。为验证改进算法的有效性，采用8个基准数学函数测试其收敛速度和收敛精度，并与GWO、CGWO和I-GWO三种灰狼算法进行对比。实验结果表明：非线性灰狼优化算法在多个测试函数上的收敛精度均达到了10-5以上，收敛精度和收敛速度优于其他三种对比算法。 |
| 关键词: 优化；非线性灰狼优化算法；反向学习机制；混沌映射 |
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中图分类号: TP181
文献标识码: A
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| 基金项目: 国家自然科学基金青年项目(62203332)；天津市自然科学基金(20JCQNJC00430)；大学生创新创业训练项目(202210069013). |
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| Nonlinear Grey Wolf Optimization Algorithm based on Chaotic Mapping and Reverse Learning Mechanism |
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DUAN Bingbing, MA Yunpeng, LIU Jinping, JIN Yin
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(College of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China)
18298884708@163.com; mayunpeng@tjcu.edu.cn; 2947469890@qq.com; 385739020@qq.com
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| Abstract: To improve the population diversity and the quality of search solutions, this paper proposes a nonlinear Grey Wolf Optimization algorithm based on Tent chaos function and reverse learning mechanism. Tent chaos function and reverse learning mechanism are used to initialize the population individuals, enhancing the distribution and diversity. A nonlinear convergence factor control strategy is introduced to balance the global search ability and local search ability. A dynamic weight strategy is introduced to improve the convergence speed and convergence ability of Grey Wolf Optimization (GWO) algorithm. To verify the effectiveness of the proposed algorithm, 8 benchmark mathematical functions are used to test its convergence speed and convergence accuracy, and it is compared with three Grey Wolf algorithms: GWO, CGWO, and I-GWO. The experimental results show that the convergence accuracy of the nonlinear Grey Wolf Optimization algorithm on multiple test functions reaches more than 10-5, and the convergence accuracy and convergence speed are better than the other three comparative algorithms. |
| Keywords: optimization; nonlinear Grey Wolf Optimization algorithm; reverse learning mechanism; chaotic mapping |
