.png){kind=icon}
返回
A fast differential evolution algorithm using k-Nearest Neighbour predictor
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2010.09.092.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Genetic algorithms (GAS), particle swarm optimisation (PSO) and differential evolution (DE) have proven to be successful in engineering optimisation problems. The limitation of using these tools is their expensive computational requirement. The optimisation process usually needs to run the numerical model and evaluate the objective function thousands of times before converging to an acceptable solution. However, in real world applications, there is simply not enough time and resources to perform such a huge number of model runs. In this study, a computational framework, known as DE-kNN, is presented for solving computationally expensive optimisation problems. The concept of DE-kNN will be demonstrated via one novel approximate model using k-Nearest Neighbour (kNN) predictor. We describe the performance of DE and DE-kNN when applied to the optimisation of a test function. The simulation results suggest that the proposed optimisation framework is able to achieve good solutions as well as provide considerable savings of the function calls compared to DE algorithm. (C) 2010 Elsevier Ltd. All rights reserved.
Keyword:
Differential evolution algorithm
k-Nearest Neighbour
Meta-model
Optimisation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
2.9W
被引数:
10.2W
机构
引用论文
Automatic calibration of a conceptual rainfall-runoff model using multiple objectives使用多个目标自动校准概念性降雨径流模型.png)
JOURNAL OF HYDROLOGY
JOURNAL OF HYDROLOGYIF6.3
PREAI
分享收藏
分享收藏
分享收藏Geminal dicationic ionic liquid functionalized graphene nanoribbon/POAP composite film: synthesis, characterization and electrochemical pseudocapacitance performance双阳离子离子液体功能化石墨烯纳米带/POAP复合膜的合成、表征及电化学赝电容性能Ionics
IonicsIF0
PREAI
分享收藏分享收藏 没有更多内容
没有更多内容