Improved Hybrid Differential Evolution-Estimation of Distribution Algorithm with Feasibility Rules for NLP/MINLP Engineering Optimization Problems

作     者：摆亮 王钧炎 江永亨 黄德先 

作者机构：Department of AutomationTsinghua University National Laboratory for Information Science and TechnologyTsinghua University Marvell Technology (Shanghai) LtdShanghai 201203China 

基　　金：Supported by the National Basic Research Program of China (2012CB720500) the National Natural Science Foundation of China (60974008) 

出 版 物：《Chinese Journal of Chemical Engineering》 (中国化学工程学报（英文版）)

年 卷 期：2012年第20卷第6期

页      码：1074-1080页

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.

主 题 词：differential evolution estimation of distribution hybrid evolution mixed-coding feasibility rules 

学科分类：07[理学] 070104[070104] 0701[理学-数学类] 

核心收录：

D　O　I：10.1016/S1004-9541(12)60589-8

馆 藏 号：203338431...