结合高斯分布的改进二进制灰狼优化算法
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摘要
针对灰狼优化算法(GWO)解决离散问题应用较少,发展不成熟的现状,提出一种用于解决二进制问题的离散灰狼优化算法(BGWO)。针对混沌搜索在解决二进制问题时,产生的初始种群较为集中的问题,引入高斯分布曲线对种群初始化,使初始种群地空间分布更加均匀;提出一种转换函数,对GWO进行二进制化处理;通过典型测试函数对该算法性能进行验证,实验表明该算法收敛精度明显优于其他算法。将该算法用于实际背包问题的求解,结论表明该算法迭代次数更少,求解精度更高。
In order to solve the problem that the Gray Wolf Optimization(GWO)algorithm is less utilized and developes immature on discrete issues, a Binary Gary Wolf Optimization(BGWO)algorithm is proposed. Firstly, aiming at the problem of chaos search that the initial population is more concentrated in solving binary problems, the Gaussian distribution curve is introduced, which makes the spatial distribution of initial population more uniform. Secondly, a transfer function is proposed to binarize the GWO. Then the performance of the algorithm is tested by the typical test function. The simulation results show that the proposed BGWO algorithm has better performance in precision. Finally, the BGWO is used to solve the knapsack problem. The conclusion shows that the BGWO has fewer iterations and higher solution accuracy.
In order to solve the problem that the Gray Wolf Optimization(GWO)algorithm is less utilized and developes immature on discrete issues, a Binary Gary Wolf Optimization(BGWO)algorithm is proposed. Firstly, aiming at the problem of chaos search that the initial population is more concentrated in solving binary problems, the Gaussian distribution curve is introduced, which makes the spatial distribution of initial population more uniform. Secondly, a transfer function is proposed to binarize the GWO. Then the performance of the algorithm is tested by the typical test function. The simulation results show that the proposed BGWO algorithm has better performance in precision. Finally, the BGWO is used to solve the knapsack problem. The conclusion shows that the BGWO has fewer iterations and higher solution accuracy.
引文
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[2] Emary E,Hossam M,Grosan C.Experienced gray wolf optimization through reinforcement learning and neural networks[J].IEEE Transactions on Neural Networks and Learning Systems,2018,29(3):681-694.
[3]王敏,唐明珠.一种新型非线性收敛因子的灰狼优化算法[J].计算机应用研究,2016,33(12):3648-3653.
[4] Vikram K K.A novel hybrid PSO-GWO approach for unit commitment problem[J].Neural Computing and Applications,2016,27(6):1643-1655.
[5]李麒玮,吴益平,苗发盛,等.基于变分模态分解与GWOMIC-SVR模型的滑坡位移预测研究[J].岩石力学与工程学报,2018,37(6).
[6]汪虹,王新远,王奉宇,等.基于PSO-GWO-SVM的周安防信号识别研究[J].激光与红外,2018,48(3):396-400.
[7]杨书杰,叶霞,李俊山,等.基于灰狼算法的BP神经网络图像恢复算法[J].微电子学与计算机,2018,35(3):19-22.
[8]姜天华.混合灰狼优化算法求解柔性作业车间调度问题[J].控制与决策,2018,33(8):503-508.
[9] Haupt T,Haupt S.Practical genetic algotithm[M].New York:John Wiley&Sons,2004.
[10] Kenney J,Eberhart R C.A discrete binary version of the particle swarm algorithm[C]//Proceedings of the IEEE International Conference on Systems,Man and Cybernetics,Orlando,USA,1997:4104-4108.
[11]陈昌帅.二进制灰狼优化算法的研究与分析[J].信息系统工程.2016,28(7):136-138.
[12] Cord J.A method for allocating funds to investment projects when returns are subject to uncertainty[J].Management Science,1964,10(2):335-341.
[13] Goldberg D E,Smith R E.Nonstationary function optimization using genetic algorithms with dominance and diploidy[C]//Proceedings of the International Conference on Genetic algorithms,Hillsdale,Canada,1987:59-68
[14] Cormen T H,Leiserson C E,Rivest R L,et al.Introduction to algorithms[M].2nd ed.Cambridge,Britain:the MIT Press,2001.
[15]李智勇,黄滔,陈少淼,等.约束优化进化算法综述[J].软件学报,2017,28(6):1529-1546.
[16] Joines J,Houck C.On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA’s[C]//Proceedings of the 1st IEEE Conference on Evolutionary Computation,1994:579-584.
[17]金惠敏,马良.遗传退火进化算法在背包问题中的应用[J].上海理工大学学报,2004,26(6):561-564.