Mε-OIDE algorithm for solving constrained optimization problems and its application in flood control operation of reservoir group

被引：0

作者：

Wang W. ^{[1
]}

Tian W. ^{[1
]}

Xu L. ^{[2
]}

Liu C. ^{[3
]}

Xu D. ^{[1
]}

机构：

[1] College of Water Resources, North China University of Water Resources and Electric Power, Zhengzhou

[2] College of Hydrology and Water Resources, Hohai University, Nanjing

[3] Research Center on Flood and Drought Disaster Reduction, China Institute of Water Resources and Hydropower Research, Beijing

来源：

Shuili Xuebao/Journal of Hydraulic Engineering | 2023年 / 54卷 / 02期

关键词：

constrained optimization; differential evolution algorithm; e constraint handling method; opposition-based learning; optimal operation of flood control; reservoir group;

D O I：

10.13243/j.cnki.slxb.20220396

中图分类号：

学科分类号：

摘要：

Constraint handling methods and a representative initial population have a significant impact on the performance of constrained optimization algorithms. Aiming at the problems of the s constraint handling method in solving constrained optimization problems such as unstable capability and difficulties in selecting empirical parameter values, this paper starts from the current two different s constraint processing methods, through the analysis of their advantages and disadvantages, the Z-e constraint processing method additionally performs 8 relaxed operations on e-quality constraints are supplemented to the overall framework of the TS-e processing method, and adds a user-defined parameter to deal with various constraint conditions, so as to propose a modified e constraint handling method. Based on the primary differential evolution algorithm, a lightweight constrained optimization algorithm named M^-OIDE was proposed by coupling it with the aforementioned modified e constraint handling method and classical opposition-based learning population initialization strategy. The test results on the CEC2006 benchmark function set verify the effectiveness of the coupling strategy, indicating that the proposed M^ -OIDE algorithm has high accuracy and strong robustness. In addition, the optimization of reservoir group flood control operation further prove that the M^-OIDE algorithm is feasible and efficient in dealing with practical constrained optimization problems. © 2023 China Water Power Press. All rights reserved.

引用

页码：148 / 158

页数：10

共 18 条

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