.png){kind=icon}
返回
A novel evolutionary algorithm based on even difference grey model
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2021.114898.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Evolutionary algorithm is an important subdomain of the meta-heuristics as a kind of the most popular optimization technology. Inspired by the grey prediction theory, this paper proposes a novel evolution algorithm based on the even difference grey model. Unlike population-based meta-heuristics with employing mutation and crossover operators to generate trial populations, the proposed algorithm develops a reproduction operator by using the even difference grey model. The novel reproduction operator regards population series as a time series. It firstly transforms an unorder data selected from the population series to a series data with approximate exponential law by using a grey operator. Based on the series data generated, an exponential model is constructed by using the even difference grey model. Finally, the reproduction operator obtains a trail population according to the prediction results of the exponential model. The effectiveness and superiority of the proposed algorithm are demonstrated on CEC2005 benchmark functions and six real-world engineering design problems. Philosophically speaking, the proposed algorithm towards a brand-new point of view to achieve the optimization process by forecasting the evolutionary direction at the macroscopic level, while the conventional evolutionary algorithms manipulate the chromosomes to realize their optimization at the microscopic level. It could be hoped from the algorithm to open a door for other prediction theories to construct new meta-heuristics.
Keyword:
Engineering design problem
Even difference grey model
Grey prediction
Macroscopic level
Optimization method
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
.png){kind=icon}
Constraint-handling in genetic algorithms through the use of dominance-based tournament selection
PREAI
分享收藏A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms关于使用非参数统计检验作为比较进化和群体智能算法的方法的实用教程
PREAI
分享收藏A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm一种求解约束工程优化问题的元启发式方法: 乌鸦搜索算法
PREAI
分享收藏
查看更多内容