用于高维空间快速求解的理性差分进化算法
摘要
本文提出了一种基于近似模型的高维求解差分进化算法。该算法使用近似模型更简单的数学映射关系来代替小区域内的原函数的复杂关系,即利用一阶近似模型在小范围内对原函数进行近似,进而用近似模型的梯度关系来指导整个解的搜索过程。这大大改进了传统的差分算法主要依赖随机搜索的特性,数学模型的理性指导使得新算法能更加快速的在高维空间收缩到最优解区域。在工程应用上,新算法将带来带来更短的学习时间,更可靠地收敛到最优解区域。根据多种类型标准测试函数的实验结果,新算法显著提高了传统DE算法在高维空间的搜索性能。
引文
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[5]Brest,S.Greiner,B.Boskovic,M.Mernik,and V.Zumer,“Self-Adapting Control Parameters in Differential Evolution:A Comparative Study on Numerical Benchmark Problems,”IEEE Transactions on Evolutionary Computation,vol.10,no.6,pp.646–657,2006.
[6]S.Das and P.N.Suganthan,“Differential evolution:A survey of the state-of-the-art,”Evolutionary Computation,IEEE Transactions on,vol.15,no.1,pp.4–31,2011.
[7]B.Subudhi and D.Jena,“Differential evolution and Levenberg Marquardt trained neural network scheme for nonlinear system identification,”Neural Processing Letters,vol.27,no.3,pp.285–296,2008.
[8]D.C.Montgomery,Design and analysis of experiments.Wiley.com,2006,p405.
[9]C.Xiao,Z.Xue,and J.Yin,‘Rational models to improve performance of differential evolution for MOEA/D’,in Natural Computation(ICNC),2014 10th International Conference on,2014,pp.335–342.
[10]I.C.Trelea,“The particle swarm optimization algorithm:convergence analysis and parameter selection,”Informatio
[11]汤伟,白志雄,高祥.基于自适应变异DE算法的PID参数整定优化[J].组合机床与自动化加工技术,2018(03):124-127.
[12]肖晓丽,吴瑶,周锡玲,廖卓凡.基于差分进化的两阶段文本特征选择算法[J/OL].计算机工程:1-9[2018-04-18].
[13]李龙澍,翁晴晴.基于反向学习的自适应差分进化算法[J].计算机应用,2018,38(02):399-404.
[14]沈鑫,邹德旋,张鑫.随机自适应差分进化算法[J].电子科技,2018,31(02):51-55.
[15]钱武文,柴军瑞,张子映,谈然.基于反射变异策略的自适应差分进化算法[J/OL].计算机工程与应用:1-9[2018-04-18].
[16]侯莹,韩红桂,乔俊飞.基于参数动态调整的多目标差分进化算法[J].控制与决策,2017,32(11):1985-1990.
[17]刘会宇,韩继红,袁霖,于波.基于双变异策略的自适应骨架差分进化算法[J].通信学报,2017,38(08):201-212.
[18]陈亮.改进自适应差分进化算法及其应用研究[D].东华大学,2012.