.png){kind=icon}
返回
Grey Wolf Optimizer
.png){kind=icon}
PRE
AI
DOI：10.1016/j.advengsoft.2013.12.007.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 29 well-known test functions, and the results are verified by a comparative study with Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), Evolutionary Programming (EP), and Evolution Strategy (ES). The results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics. The paper also considers solving three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) and presents a real application of the proposed method in the field of optical engineering. The results of the classical engineering design problems and real application prove that the proposed algorithm is applicable to challenging problems with unknown search spaces. (C) 2013 Elsevier Ltd. All rights reserved.
Keyword:
Optimization
Optimization techniques
Heuristic algorithm
Metaheuristics
Constrained optimization
GWO
期刊
IF:
5.7
论文数:
3.3K
被引数:
1.2W
机构
引用论文
.png){kind=icon}
Purification of Copper Nanowires To Prepare Flexible Transparent Conductive Films with High Performance提纯铜纳米线制备高性能柔性透明导电薄膜
PREAI
分享收藏Constraint-handling in genetic algorithms through the use of dominance-based tournament selection
PREAI
分享收藏
查看更多内容