| 王文川,田维璨,徐雷,刘昌军,徐冬梅.Mε-OIDE求解约束优化问题算法及其在水库群防洪调度中的应用[J].水利学报,2023,54(2):148-158 |
| Mε-OIDE求解约束优化问题算法及其在水库群防洪调度中的应用 |
| Mε-OIDE algorithm for solving constrained optimization problems and its application in flood control operation of reservoir group |
| 投稿时间：2022-05-20 |
| DOI：10.13243/j.cnki.slxb.20220396 |
| 中文关键词: 约束优化 ε约束处理法 差分进化算法 反向学习 水库群 防洪优化调度 |
| 英文关键词: constrained optimization ε constraint handling method differential evolution algorithm opposition-based learning reservoir group optimal operation of flood control |
| 基金项目:河南省重点研发与推广专项（202102310259）；河南省高校科技创新团队项目（18IRTSTHN009） |
|
| 摘要点击次数: 2049 |
| 全文下载次数: 894 |
| 中文摘要: |
| 约束处理技术和初始种群代表性对约束优化算法的性能具有重要影响。针对ε约束处理法求解约束优化问题时结果不稳定、经验参数难取值等问题，本文首先从当前两种不同的ε约束处理法出发，通过对其优缺点的分析，将Z-ε约束处理法对等式约束额外进行δ放松的操作补充到TS-ε处理法的整体框架中，并增设一个用户自定义参数来处理多样的约束条件，从而提出了一种改进的ε约束处理法。基于原始的差分进化算法，将其与前述改进的ε约束处理法和经典的反向学习初始化种群策略耦合，提出一种轻量化的Mε-OIDE （Modified ε-Opposition-based-learning Initialization Differential Evolution）约束优化算法。在CEC2006基准函数集上的测试结果验证了耦合策略的有效性，表明提出的Mε-OIDE算法具有高精度和强鲁棒性。此外，在水库群防洪调度问题上的优化结果进一步证明了Mε-OIDE优化算法处理实际约束优化问题的可行性和高效性。 |
| 英文摘要: |
| 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 ε 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 ε constraint processing methods, through the analysis of their advantages and disadvantages, the Z-ε constraint processing method additionally performs δ relaxed operations on equality constraints are supplemented to the overall framework of the TS-ε processing method, and adds a user-defined parameter to deal with various constraint conditions, so as to propose a modified ε constraint handling method.Based on the primary differential evolution algorithm, a lightweight constrained optimization algorithm named Mε-OIDE was proposed by coupling it with the aforementioned modified ε 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 Mε-OIDE algorithm has high accuracy and strong robustness.In addition, the optimization of reservoir group flood control operation further prove that the Mε-OIDE algorithm is feasible and efficient in dealing with practical constrained optimization problems. |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |