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基于差分进化和分布估计的改进混合算法在NLP及MINLP工程优化问题中的应用(英文)
引用本文:摆亮,王钧炎,江永亨,黄德先.基于差分进化和分布估计的改进混合算法在NLP及MINLP工程优化问题中的应用(英文)[J].中国化学工程学报,2012,20(6):1074-1080.
作者姓名:摆亮  王钧炎  江永亨  黄德先
作者单位:1. Department of Automation, Tsinghua University, Beijing 100084, China;2. National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China;3. Marvell Technology (Shanghai) Ltd, Shanghai 201203, China
基金项目:Supported by the National Basic Research Program of China (2012CB720500);the National Natural Science Foundation of China (60974008)
摘    要:In this paper, an improved hybrid differential evolution-estimation of distribution algorithm (IHDE-EDA) is proposed for nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) models in engineering optimization fields. In order to improve the global searching ability and convergence speed, IHDE-EDA takes full advantage of differential information and global statistical information extracted respectively from differential evolution algorithm and annealing mechanism-embedded estimation of distribution algorithm. Moreover, the feasibility rules are used to handle constraints, which do not require additional parameters and can guide the population to the feasible region quickly. The effectiveness of hybridization mechanism of IHDE-EDA is first discussed, and then simulation and comparison based on three benchmark problems demonstrate the efficiency, accuracy and robustness of IHDE-EDA. Finally, optimization on an industrial-size scheduling of two-pipeline crude oil blending problem shows the practical applicability of IHDE-EDA.

关 键 词:differential  evolution  estimation  of  distribution  hybrid  evolution  mixed-coding  feasibility  rules  
收稿时间:2012-05-28

Improved Hybrid Differential Evolution-Estimation of Distribution Algorithm with Feasibility Rules for NLP/MINLP Engineering Optimization Problems*
BAI Liang,WANG Junyan,JIANG Yongheng,and HUANG Dexian.Improved Hybrid Differential Evolution-Estimation of Distribution Algorithm with Feasibility Rules for NLP/MINLP Engineering Optimization Problems*[J].Chinese Journal of Chemical Engineering,2012,20(6):1074-1080.
Authors:BAI Liang  WANG Junyan  JIANG Yongheng  and HUANG Dexian
Affiliation:1. Department of Automation, Tsinghua University, Beijing 100084, China;2. National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China;3. Marvell Technology (Shanghai) Ltd, Shanghai 201203, China
Abstract:In this paper,an improved hybrid differential evolution-estimation of distribution algorithm (IHDE-EDA) is proposed for nonlinear programming (NLP) and mixed integer nonlinear programming (MINLP) models in engineering optimization fields.In order to improve the global searching ability and convergence speed,IHDE-EDA takes full advantage of differential information and global statistical information extracted respectively from differential evolution algorithm and annealing mechanism-embedded estimation of distribution algorithm.Moreover,the feasibility rules are used to handle constraints,which do not require additional parameters and can guide the population to the feasible region quickly.The effectiveness of hybridization mechanism of IHDE-EDA is first discussed,and then simulation and comparison based on three benchmark problems demonstrate the efficiency,accuracy and robustness of IHDE-EDA.Finally,optimization on an industrial-size scheduling of two-pipeline crude oil blending problem shows the practical applicability of IHDE-EDA.
Keywords:
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