摘要
为求解复杂函数计算问题,很多启发式算法得以发展,灰狼优化算法因其实现简单、算法寻优能力强等特点得以广泛应用。为提高灰狼优化算法求解精度,改进灰狼优化算法在后期收敛速度较慢的问题,本文讨论了迭代过程全局搜索与局部搜索所占比例对灰狼优化算法的影响,将一种新型收敛因子应用于算法中,提出一种改进的灰狼优化算法。最后,采用测试集的13个标准测试函数对改进后的算法进行仿真实验,实验结果表明,本文提出的改进的灰狼优化算法在求解精度和算法稳定性等指标优于对比算法。
Many heuristic algorithms are developed to solve the evaluation of complex functions. Grey wolf optimizer is widely used for its remarkable advantages such as simple used and favorable optimizing ability. To improve the problem of slow convergence speed in later period of iterations and enhance the precision of optimization in grey wolf optimizor, a modified algorithm is proposed after discussing the ratio of global search and local search in iterations and apply a new convergence factor to initial algorithm. Simulation experiments of 13 standard test functions is conducted of which the result shows that the proposed improved grey wolf optimization algorithm has better performance.
作者
邢燕祯
王东辉
XING Yanzhen;WANG Donghui(University of Chinese Academy of Sciences,Institute of Acoustics,Chinese Academy of Sciences,Beijing,100190,China;Key laboratory of Technology for Autonomous Underwater Vehicles,Institute of Acoustics,Beijing,100190,China)
出处
《网络新媒体技术》
2020年第3期28-34,共7页
Network New Media Technology
关键词
灰狼优化算法
启发算法
收敛因子
全局优化算法
Grey Wolf Optimizor
Heuristic Algorithm
Convergence factor
Global Optimization Algorithm
作者简介
邢燕祯,(1992-),女,博士研究生,主要研究方向:智能计算、最优化算法。;王东辉,(1973-),男,研究员,博士生导师,主要研究方向:机器学习,DSP处理器设计,超大规模集成电路等。
