.png){kind=icon}
返回
The defect of the Grey Wolf optimization algorithm and its verification method
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2019.01.018.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Grey wolf optimization algorithm (GWO) is a new meta-heuristic optimization technology. Its principle is to imitate the behavior of grey wolves in nature to hunt in a cooperative way. GWO is different from others in terms of model structure. It is a large-scale search method centered on three optimal samples, and which is also the research object of many scholars. In the course of its research, this paper find that GWO is flawed. It has good performance for the optimization problem whose optimal solution is 0, however, for other problems, its advantage is not as obvious as before or even worse. Then it is further found that when GWO solves the same optimization function, the farther the function's optimal solution is from 0, the worse its performance, and this flaw also appears in other optimization algorithms. Through the study of this defect, the analysis is carried out, and the reason is determined. Finally, although there is no way to make GWO normal, this paper provides a verification method to avoid the same problem, and hopes to help the development of the optimization algorithm. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Meta-heuristic
Gray Wolf optimization algorithm
Optimization algorithm
Defect
New verification 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
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
分享收藏An efficient modified grey wolf optimizer with Levy flight for optimization tasks带有Levy飞行的高效改进的灰狼优化器，用于优化任务
PREAI
分享收藏分享收藏Chaotic opposition-based grey-wolf optimization algorithm based on differential evolution and disruption operator for global optimization基于差分进化和破坏算子的混沌反向灰狼优化算法的全局优化
PREAI
分享收藏The effects of obesity and standing time on postural sway during prolonged quiet standing.png)
Ergonomics
ErgonomicsIF0
PREAI
分享收藏
查看更多内容