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

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 converg...

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Bibliographic Details
Published inChinese journal of chemical engineering Vol. 20; no. 6; pp. 1074 - 1080
Main Author 摆亮 王钧炎 江永亨 黄德先
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.12.2012
Subjects
Algorithms
Annealing
Computer simulation
Crude oil
differential evolution
estimation of distribution
Feasibility
feasibility rules
hybrid evolution
MINLP
mixed-coding
Nonlinear programming
Optimization
Searching
优化问题
全局搜索能力
分布估计算法
工程优化
差分进化算法
混合整数非线性规划
统计信息
differential evolution
mixed-coding
feasibility rules
estimation of distribution
hybrid evolution
Online AccessGet full text
ISSN1004-9541
2210-321X
DOI10.1016/S1004-9541(12)60589-8

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Summary: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.
Bibliography:BAI Liang, , WANG Junyan , JIANG Yongheng, and HUANG Dexian, Department of Automation, Tsinghua University, Beijing 000, China National Laboratory for Information Science and Technology, Tsinghua University, Beijing 000, China Marvell Technology （Shanghai） Ltd, Shanghai 201203, China
differential evolution; estimation of distribution; hybrid evolution; mixed-coding; feasibility rules
11-3270/TQ
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.
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ISSN:1004-9541
2210-321X
DOI:10.1016/S1004-9541(12)60589-8