一种高效动态自适应差分进化算法
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摘要
针对差分进化算法易早熟收敛、收敛精度低等特点,文中提出一种高效动态自适应差分进化(EDSDE)算法。该算法从变异因子、变异策略以及交叉因子方面入手,将变异因子设置成线性递减函数,在基向量前加入幅值系数以平衡全局搜索和局部搜索,将交叉因子设置成在[0,1]内不断震荡且每隔50代更新一次的动态自适应函数。仿真实验结果表明,EDSDE能获得更好的优化结果,且比其他算法具有更好的优化性能。
This paper proposed an efficient dynamic self-adaptive differential evolution(EDSDE) algorithm based on the characteristics of premature convergence,low convergence accuracy.The algorithm starts with mutation factor,mutation strategy and crossover factor.It sets the mutation factor to a linear decreasing function,incorporates an amplitude coefficient into the base vector to balance the global search and the local search,and sets the crossover factor to a dynamic self-adaptive function that is constantly oscillated within [0,1] and updated every 50 generations.The simulation results show that EDSDE can obtain better optimization results and exhibit more desirable performance then the other algorithms.
This paper proposed an efficient dynamic self-adaptive differential evolution(EDSDE) algorithm based on the characteristics of premature convergence,low convergence accuracy.The algorithm starts with mutation factor,mutation strategy and crossover factor.It sets the mutation factor to a linear decreasing function,incorporates an amplitude coefficient into the base vector to balance the global search and the local search,and sets the crossover factor to a dynamic self-adaptive function that is constantly oscillated within [0,1] and updated every 50 generations.The simulation results show that EDSDE can obtain better optimization results and exhibit more desirable performance then the other algorithms.
引文
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[2] 霍玉洪.切比雪夫不等式及其应用[J].长春工业大学学报:自然科学版,2012,33(6):713-714.
[3] 曲良东,何登旭,吴尽昭.一种群模式全局搜索算法[J].模式识别与人工智能,2013,26(6):592-597.
[4] ZOU D X,GAO L Q.An Efficient Improved Differential Evolution Algorithm[C]//第三十一届中国控制会议.Hefei,China,2012:2385-2390.
[5] 孙成富,赵建洋,高磊.基于变权重因子差分进化算法的梯级水火电力系统调度[J].数据采集与处理,2017,32(1):95-103.
[6] 卢有麟,周建中,覃晖,等.差分进化算法在电力系统环境经济调度中的应用[J].华中科技大学学报(自然科学版),2010,38(8):121-124.
[7] 张锟,徐青,王一凡,等.自适应差分进化算法在边坡滑面搜索中的应用[J].岩土力学,2017,38(5):1503-1509.
[8] 翁志远,方杰,孔敏,等.改进差分进化算法的作业车间调度优化策略[J].控制工程,2017,24(6):1282-1285.
[9] 郭丽.自适应差分进化算法解决多目标有限缓冲车间调度问题研究[D].郑州:郑州大学,2016.
[10] 吴娜,车蕾.基于改进DDE算法的协同干扰资源分配[J].电光与控制,2018,25(2):107-110.
[11] 邹德旋,王鑫,段纳.一种基于修正差分进化的虹膜定位算法[J].控制理论与应用,2013,30(9):1194-1200.
[12] 李目,何怡刚,周少武,等.一种差分进化算法优化小波神经网络及其在弱信号检测中的应用[J].计算机应用与软件,2010,27(3):29-31,39.
[13] 陈亮.改进自适应差分进化算法及其应用研究[D].上海:东华大学,2012.
[14] 刘龙龙,颜七笙.一种新的改进差分进化算法[J].江西科学,2017,35(4):485-489.
[15] 欧阳海滨,高立群,孔祥勇.随机变异差分进化算法[J].东北大学学报:自然科学版,2013,34(3):330-334.
[16] BREST J,GREINER S,BOSKOVIC B,et al.Self-adapting control parameters in differential evolution:A comparative study on numerical benchmark problems[J].IEEE Transactions on Evolutionary Computation,2006,10(6):646-657.
[17] ZHANG J,SANDERSON A C.JADE:adaptive differential evolution with optional external archive[J].IEEE Transactions on Evolutionary Computation,2009,13(5):945-958.
[18] ISLAM S M,DAS S,GHOSH S,et al.An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B(Cybernetics),2012,42(2):482-500.
[19] RAHNAMAYAN S,TIZHOOSH H R,SALAMA M M A.Opposition-Based Differential Evolution[M].Springer Berlin Heidelberg,2008.
[20] ALI M M,KHOMPATRAPORN C,ZABINSKY Z B.A Nu- merical Evaluation of Several Stochastic Algorithms on Selected Continuous Global Optimization Test Problems[J].Journal of Global Optimization,2005,31(4):635-672.
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