Grey Wolf Optimization Algorithm Based on Sine Factor and Quantum Local Search

doi:10.3778/j.issn.1002-8331.2012-0080

Abstract

Abstract:

In order to solve complex problems, the gray wolf optimization algorithm has some shortcomings, such as relying on the initial population, low convergence accuracy and getting easily trapped into local optima. An improved grey wolf optimization algorithm （Quantum Gray Wolf Optimization Algorithm, QGWO） combining sinusoidal control factor and quantum local search is proposed. The control factors of Gray Wolf algorithm are changed according to the curve with sine change. The improved algorithm accelerates the convergence speed in the early stage of iteration to complete the global exploration quickly, and slows down the convergence speed in the late iteration to improve the accuracy of the algorithm. At the same time, quantum local search is introduced to reduce the probability of the algorithm falling into local optimum. Then, twelve standard test functions are selected to verify the performance of QGWO algorithm, and the single peak, multi peak and fixed dimension test functions are compared. The experimental results show that compared with GWO, WOA, SCA and CGWO, QGWO has higher accuracy and stability in solving test functions. Finally, an engineering example is used to optimize KELM for classification experiments. The results show that QGWO has better optimization performance.

Key words: improved grey wolf optimization algorithm, sine factor, quantum local search, test function

摘要：

针对基本灰狼优化算法在求解复杂问题时，存在依赖初始种群、过早收敛和易陷入局部最优等缺点，提出一种融合正弦控制因子和量子局部搜索的灰狼优化算法（QGWO）。通过对灰狼算法中的控制因子按照具有正弦变化的曲线变化，使改进后的算法在迭代前期加快收敛速度以快速完成全局搜索，并且在迭代后期减缓收敛速度以提高算法精度。引入量子局部搜索降低算法陷入局部最优的概率。选用12个标准测试函数对QGWO算法性能进行验证，分别从单峰、多峰和固定维测试函数对比分析。实验结果表明，与GWO、WOA、SCA和CGWO相比，QGWO对测试函数的求解有更高的精度和稳定性。通过工程实例优化KELM进行分类实验验证，QGWO表现出更好的寻优性能。

关键词: 改进灰狼优化算法, 正弦因子, 量子局部搜索, 测试函数

XU Chenhua, LUO Zhuguang, WU Guanhong, LIU Bin. Grey Wolf Optimization Algorithm Based on Sine Factor and Quantum Local Search[J]. Computer Engineering and Applications, 2021, 57(24): 83-89.

徐辰华，骆珠光，吴冠宏，刘斌. 基于正弦因子和量子局部搜索的灰狼优化算法[J]. 计算机工程与应用, 2021, 57(24): 83-89.

References

[1] MIRJALILI S，MIRJALILI S M，LEWIS A.Grey wolf optimizer[J].Advances in Engineering Software，2014，69：46-61.
[2] 吴继浩，杨涛.基于改进灰狼算法求解柔性车间调度问题[J].制造业自动化，2019，41（4）：101-105.
WU J H，YANG T.Improved grey wolf optimization algorithm for flexible shop scheduling problem[J].Manufacturing Automation，2019，41（4）：101-105.
[3] LU C，GAO L，LI X，et al.A hybrid multi-objective grey wolf optimizer for dynamic scheduling in a real-world welding industry[J].Engineering Applications of Artifificial Intelligence，2017，57（C）：61-79.
[4] 徐达宇，丁帅.改进GWO优化SVM的云计算资源负载短期预测研究[J].计算机工程与应用，2017，53（7）：68-73.
XU D Y，DING S.Research on improved GWO-optimized SVM-based short-term load prediction for cloud computing[J].Computer Engineering and Applications，2017，53（7）：68-73.
[5] 龙文，蔡绍洪，焦建军，等.求解高维优化问题的混合灰狼优化算法[J].控制与决策，2016，31（11）：1991-1997.
LONG W，CAI S H，JIAO J J，et al.Hybrid grey wolf optimization algorithm for high-dimensional optimization[J].Control and Decision，2016，31（11）：1991-1997.
[6] 张文胜，郝孜奇，朱冀军，等.基于改进灰狼算法优化BP神经网络的短时交通流预测模型[J].交通运输系统工程与信息，2020，20（2）：196-203.
ZHANG W S，HAO Z Q，ZHU J J，et al.BP neural network model for short-time traffic flow forecasting based on transformed grey wolf optimizer algorithm[J].Journal of Transportation Systems Engineering and Information Technology，2020，20（2）：196-203.
[7] 方一鸣，赵晓东，张攀，等.基于改进灰狼算法和多核极限学习机的铁水硅含量预测建模[J].控制理论与应用，2020，37（7）：1644-1654.
FANG Y M，ZHAO X D，ZHANG P，et al.Prediction modeling of silicon content in liquid iron based on multiple kernel extreme learning machine and improved grey wolf optimizer[J].Control Theory & Applications，2020，37（7）：1644-1654.
[8] JAYABARATHI T，RAGHUNATHAN T，ADARSH B R，et al.Economic dispatch using hybrid grey wolf optimizer[J].Energy，2016，111：630-641.
[9] 张铸，饶盛华，张仕杰.基于自适应正态云模型的灰狼优化算法[J].控制与决策，2021，36（10）：2562-2568.
ZHANG Z，RAO S H，ZHANG S J.Grey wolf optimization algorithm based on adaptive normal cloud model[J].Control and Decision，2021，36（10）：2562-2568.
[10] LONG W，JIAO J，LIANG X，et al.An exploration-enhanced grey wolf optimizer to solve high-dimensional numerical optimization[J].Engineering Applications of Artificial Intelligence，2018，68：63-80.
[11] 刘勇，马良.转换参数非线性递减的正弦余弦算法[J].计算机工程与应用，2017，53（2）：1-5.
LIU Y，MA L.Sine cosine algorithm with nonlinear decreasing conversion parameter[J].Computer Engineering and Applications，2017，53（2）：1-5.
[12] MIRJALILI S，LEWIS A.The whale optimi zation algorithm[J].Advances in Engineering Software，2016，95：51-67.
[13] 徐辰华，李成县，王尤军，等.基于混沌灰狼优化算法的氧化铝质量指标预测模型[J].广西大学学报（自然科学版），2016，41（6）：1869-1878.
XU C H，LI C X，WANG Y J，et al.Quality prediction model of alumina sintering process based on chaotic grey wolf optimization algorithm[J].Journal of Guangxi University（Natural Science Edition），2016，41（6）：1869-1878.
[14] 程知.数据挖掘的预处理技术研究[J].计算机光盘软件与应用，2014（6）：115-117.
CHENG Z.Research on preprocessing technology of data mining[J].Computer CD Software and Applications，2014（6）：115-117.
[15] 郭伯勋，李军.基于KELM的位置指纹室内定位方法研究[J].计算机工程与应用，2016，52（9）：78-83.
GUO B X，LI J.Kernel extreme learning machine for indoor positioning in location fingerprinting[J].Computer Engineering and Applications，2016，52（9）：78-83.