一种新的群智能算法:狮群算法
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摘要
随着优化对象变得非线性化、高维化、多目标化,传统的优化方法越来越难以得到理想的优化结果。群智能算法能够很好地弥补传统优化方法的缺陷。文中提出了一种新的群智能算法——狮群算法。狮群算法对初值的要求不高,算法的寻优速度较快,有较强的全局寻优能力。给出了狮群算法的原理和详细描述,对算法的收敛性能进行了分析,并将其与人工蜂群算法做了对比。最后,将所提算法应用到光伏最大功率跟踪中,通过实验和仿真验证了其实际寻优能力。
As the optimization object becomes nonlinear,high dimensional and multi target,the ideal result can not be obtained by using the traditional optimization method.Intelligent algorithm is a good solution to the shortcomings of the traditional optimization methods.This paper proposed a new intelligent algorithm,called lions algorithm.Lions algorithm's request on the initial value is not high.It has faster optimization speed and strong global convergence ability.In this paper,the principle of the lions algorithm was given,the convergence performance of the algorithm and the influence of the parameters on the convergence of the algorithm were analyzed,and it was compared with artificial bee colony algorithm.Finally,the algorithm was applied to the maximum power tracking of the PV,and the practical ability of the algorithm was verified by experiment and simulation.
As the optimization object becomes nonlinear,high dimensional and multi target,the ideal result can not be obtained by using the traditional optimization method.Intelligent algorithm is a good solution to the shortcomings of the traditional optimization methods.This paper proposed a new intelligent algorithm,called lions algorithm.Lions algorithm's request on the initial value is not high.It has faster optimization speed and strong global convergence ability.In this paper,the principle of the lions algorithm was given,the convergence performance of the algorithm and the influence of the parameters on the convergence of the algorithm were analyzed,and it was compared with artificial bee colony algorithm.Finally,the algorithm was applied to the maximum power tracking of the PV,and the practical ability of the algorithm was verified by experiment and simulation.
引文
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[3]KARABOGA D.An idea based on honey bee swarm for numerical optimization[R].Technical Report TR06,Erciyes University,2005.
[4]葛宇,梁静,王学平,等.改进人工蜂群算法求解多目标连续优化问题[J].计算机科学,2014,41(6):254-259.
[5]YANG X S.Firefly algorithm,stochastic test functions and design Optimization[J].Bio-Inspired Computation,2010,2(2):78-84.
[6]程美英,倪志伟朱旭辉.萤火虫优化算法理论研究综述[J].计算机科学,2015,42(4):19-24.
[7]PUNITHA K,DEVARAJ D,SAKTHIVEL S.Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions[J].Energy,2013(62):330-340.
[8]HIREN P,VIVEK A.Maximum Power Point Tracking Scheme for PV Systems Operating Under Partially Shaded Conditions[J].IEEE Transactions on Industrial Electronics,2008,55(4):1689-1698.
[2]DORIGO M,GAMBARDELLA L M.Ant colony system:a cooperative learning approach to the traveling salesman problem[J].IEEE Transactions on Evolutionary Computation,1997,1(1):53-66.
[3]KARABOGA D.An idea based on honey bee swarm for numerical optimization[R].Technical Report TR06,Erciyes University,2005.
[4]葛宇,梁静,王学平,等.改进人工蜂群算法求解多目标连续优化问题[J].计算机科学,2014,41(6):254-259.
[5]YANG X S.Firefly algorithm,stochastic test functions and design Optimization[J].Bio-Inspired Computation,2010,2(2):78-84.
[6]程美英,倪志伟朱旭辉.萤火虫优化算法理论研究综述[J].计算机科学,2015,42(4):19-24.
[7]PUNITHA K,DEVARAJ D,SAKTHIVEL S.Artificial neural network based modified incremental conductance algorithm for maximum power point tracking in photovoltaic system under partial shading conditions[J].Energy,2013(62):330-340.
[8]HIREN P,VIVEK A.Maximum Power Point Tracking Scheme for PV Systems Operating Under Partially Shaded Conditions[J].IEEE Transactions on Industrial Electronics,2008,55(4):1689-1698.