一种基于多代理模型的混合整数规划优化方法
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摘要
提出一种基于多代理模型的优化方法,求解混合整数规划问题.首先,基于群智能优化策略提出一种基于多群体协作模型的采样方法,保证候选解的正确性和多样性;其次,采用基于数据并行的高斯过程建模方法,在线构造局部代理模型;再次,通过多代理模型对候选解进行预筛选,实现与粒子群算法的协同优化;最后,通过14个测试问题和一个基于数据驱动的模型参数选取问题,验证所提出方法的有效性.
A multi-surrogates algorithm is developed to deal with the mixed-integer programming problems. Firstly, a sampling method based on the model of the multi-swarm PSO is developed to ensure the accuracy and diversity of the the samples. Furthermore, the local surrogate models are constructed by an online modeling method based on the data parallel approach. Then, the collaborative optimization is carried out based on the preselecting strategy and PSO. Finally,the effectiveness of the proposed method are verified by the 14 test problems and 1 data driven model parameter selection problems.
A multi-surrogates algorithm is developed to deal with the mixed-integer programming problems. Firstly, a sampling method based on the model of the multi-swarm PSO is developed to ensure the accuracy and diversity of the the samples. Furthermore, the local surrogate models are constructed by an online modeling method based on the data parallel approach. Then, the collaborative optimization is carried out based on the preselecting strategy and PSO. Finally,the effectiveness of the proposed method are verified by the 14 test problems and 1 data driven model parameter selection problems.
引文
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[2]Holmstr?m K,Quttineh N H,Edvall M MAn adaptive radial basis algorithm(ARBF)for expensive black-box mixed-integer constrained globa optimization[J].Optimization&Engineering,2008,9(4):311-339.
[3]Müller J,Shoemaker C A,PichéR.SO-MI:Asurrogate model algorithm for computationally expensive nonlinear mixed-integer black-box global optimization problems[J].Computers&Operations Research,2013,40(5):1383-1400.
[4]Haftka R T,Villanueva D,Chaudhuri A.Parallel surrogate-assisted global optimization with expensive functions--A survey[J].Structural&Multidisciplinary Optimization,2016,54(1):3-13.
[5]Zhou Z,Ong Y S,Nair P B,et al.Combining global and local surrogate models to accelerate evolutionary optimization[J].IEEE Trans on Systems,Man,and Cybernetics,Part C,2007,37(1):66-76.
[6]Liem R P,Mader C A,Martins J R R A.Surrogate models and mixtures of experts in aerodynamic performance prediction for aircraft mission analysis[J].Aerospace Science and Technology,2015,43(8):126-151.
[7]Chowdhury S,Tong W,Messac A,et al.A mixed-discrete particle swarm optimization algorithm with explicit diversity-preservation[J].Structural&Multidisciplinary Optimization,2013,47(3):367-388.
[8]Tang Y F,Chen J Q,Wei J H.A surrogate-based particle swarm optimization algorithm for solving optimization problems with expensive black box functions[J].Engineering Optimization,2013,45(5):557-576.
[9]Clerc M,Kennedy J.The particle swarm-explosion,stability,and convergence in a multidimensional complex space[J].IEEE Trans on Evolutionary Computation,2002,6(1):58-73.
[10]Choudhury A,Nair P B,Keane A J.A data parallel approach for large-scale gaussian process modeling[C].Siam Int Conf on Data Mining.Arlington,2002:95-111.
[11]Petelin D.Gaussian processes for machine learning[J].Int J of Neural Systems,2006,14(6):3011-3015.
[12]Bussieck M R,Drud A S,Meeraus A.MINLPLib--A collection of test models for mixed-integer nonlinear programming[J].Informs J on Computing,2003,15(1):114-119.
[13]Adjiman C S,Androulakis I P,Floudas C A.Global optimization of mixedinteger nonlinear problems[J].AIChE Journal,2000,46(9):1769-1797.
[14]Mezura-Montes E,Coello C A C.A simple multimembered evolution strategy to solve constrained optimization problems[J].IEEE Trans on Evolutionary Computation,2005,9(1):1-17.
[15]Zhao J,Wang W,Pedrycz W,et al.Online parameter optimization-based prediction for converter gas system by parallel strategies[J].IEEE Trans on Control Systems Technology,2012,20(3):835-845.
[16]Bergstra J,Bengio Y.Algorithms for hyper-parameter optimization[C].Int Conf on Neural Information Processing Systems.Granda,2011:2546-2554.