引入迁移和变异策略的改进鸟群算法及其在参数估计中的应用
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摘要
针对鸟群算法(BSA)易陷入局部最优的问题,提出了一种引入迁移策略和变异策略的改进鸟群算法(IBSA)。在鸟群飞行阶段引入迁移策略有助于提高鸟群向适应度更高位置迁移的能力,提高BSA的收敛速度;在寻优后期引入变异策略,提高鸟群的局部寻优能力,提高了算法的寻优能力。选取6个典型的测试函数进行寻优实验,实验结果表明,与粒子群算法(PSO)、蝙蝠算法(BA)、BSA等算法相比,IBSA具有更高的寻优精度和更快的寻优速度。在此基础上,将IBSA应用于发酵动力学模型参数估计中,与Gauss-Newton、GA、MAEA算法相比,IBSA的参数估计值的偏差平方和最小,具有更高的模型拟合精度。在面对非凸、不可微等复杂寻优问题的情况下,IBSA为研究者提供了一种更加可靠、快速和精确的寻优可能。
In order to deal with the shortcoming of the local optima of the bird swarm algorithm(BSA),an improved bird swarm algorithm(IBSA)is proposed in this paper by introducing the migration strategy and the mutation strategy.In the stage of flight,the migration strategy is adopted to raise the ability of bird swarm migration and the convergence speed of BSA.In the later stage of convergence of the BSA,the mutation strategy is utilized to optimize the local searching of the bird swarm and improve the searching ability of the proposed algorithm.Six typical test functions are selected to perform the optimization experiments which are implemented by particle swarm optimization(PSO),bat algorithm(BA),BSA,and IBSA,respectively.It is shown from the above experimental results that IBSA has the highest convergence precision and the fastest searching speed.Finally,IBSA is used to estimate the parameters of the fermentation kinetic models.Compared with Gauss-Newton,GA and MAEA,IBSA can obtain the smallest value of square sum of deviations squares.Hence,IBSA has the highest model fitting precision and the highest model fitting accuracy of the four algorithms,which also means that IBSA is a reliable,fast and accurate optimization tool for complex optimization problems such as the non-convex and the non-differentiable.
In order to deal with the shortcoming of the local optima of the bird swarm algorithm(BSA),an improved bird swarm algorithm(IBSA)is proposed in this paper by introducing the migration strategy and the mutation strategy.In the stage of flight,the migration strategy is adopted to raise the ability of bird swarm migration and the convergence speed of BSA.In the later stage of convergence of the BSA,the mutation strategy is utilized to optimize the local searching of the bird swarm and improve the searching ability of the proposed algorithm.Six typical test functions are selected to perform the optimization experiments which are implemented by particle swarm optimization(PSO),bat algorithm(BA),BSA,and IBSA,respectively.It is shown from the above experimental results that IBSA has the highest convergence precision and the fastest searching speed.Finally,IBSA is used to estimate the parameters of the fermentation kinetic models.Compared with Gauss-Newton,GA and MAEA,IBSA can obtain the smallest value of square sum of deviations squares.Hence,IBSA has the highest model fitting precision and the highest model fitting accuracy of the four algorithms,which also means that IBSA is a reliable,fast and accurate optimization tool for complex optimization problems such as the non-convex and the non-differentiable.
引文
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[4]YANG X S.Firefly algorithm,Lévy flights and global optimization[C]//Research and Development in Intelligent Systems XXVI.London:Springer,2010:209-218.
[5]孔祥东,钱锋.基于寄生行为的双种群萤火虫算法及其在柴油调合中的应用[J].华东理工大学学报(自然科学版),2017,43(2):213-219.
[6]YANG X S,DEB S.Cuckoo search via Lévy flights[C]//NaBIC 2009 World Congress on Nature&Biologically Inspired Computing.UK:IEEE,2010:210-214.
[7]YANG X S,DEB S.Engineering optimisation by Cuckoo search[J].International Journal of Mathematical Modelling&Numerical Optimisation,2010,1(4):330-343.
[8]YANG X S.A new metaheuristic bat-inspired algorithm[J].Physics,2010,284:65-74.
[9]胡飞,孙自强.一种基于椋鸟群行为的改进型蝙蝠算法[J].华东理工大学学报(自然科学版),2017,43(4):525-532.
[10]MENG X,LIU Y,GAO X,et al.A new bio-inspired algorithm:Chicken swarm optimization[J].Advances in Swarm Intelligence,2014,8794:86-94.
[11]MENG X B,GAO X Z,LU L,et al.A new bio-inspired optimisation algorithm:Bird swarm algorithm[J].Journal of Experimental&Theoretical Artificial Intelligence,2015,12(17):673-687.
[12]崔东文,金波.改进鸟群算法及其在梯级水库优化调度中的应用[J].三峡大学学报(自然科学版),2016,38(6):7-14.
[13]刘晓龙,宁芊,赵成萍,等.基于莱维飞行的鸟群优化算法[J].计算机测量与控制,2016,24(12):194-197.
[14]屈迟文,傅彦铭.基于混合变异算子的布谷鸟优化算法[J].科学技术与工程,2013,13(27):8008-8013.
[15]DAN S,OMRAN M G H,CLERC M.Linearized biogeography-based optimization with re-initialization and local search[J].Information Sciences,2014,267:140-157.
[16]MACARTHUR R H,WILSON E O.The theory of island biogeography[J].Bioscience,1967,18(3):522-542.
[17]付国江,王少梅,刘舒燕,等.含维变异算子的粒子群算法[J].武汉大学学报(工学版),2005,38(4):79-83.
[18]梁昔明,李朝辉,龙文,等.含维变异算子的连续域蚁群算法[J].计算机应用,2010,30(12):3204-3206.
[19]宋文军,陈宁,王健,等.L-色氨酸产生菌分批发酵动力学模型[J].食品与生物技术学报,2002,21(4):340-343.
[20]刘德玲,谢盛嘉,关晓颖.自适应域多群体遗传算法求解发酵动力学模型参数[J].计算技术与自动化,2010,29(3):18-23.
[21]何鹏飞,李绍军.融合差分进化算法的AEA算法及其在参数估计中的应用[J].化工学报,2014,65(12):4857-4865.
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