.png){kind=icon}
返回
Greylag Goose Optimization: Nature-inspired optimization algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2023.122147.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Nature-inspired metaheuristic approaches draw their core idea from biological evolution in order to create new and powerful competing algorithms. Such algorithms can be divided into evolution-based and swarm-based algorithms. This paper proposed a new nature-inspired optimizer called the Greylag Goose Optimization (GGO) algorithm. The proposed algorithm (GGO) belongs to the class of swarm-based algorithms and is inspired by the Greylag Goose. Geese are excellent flyers and during their seasonal migrations, they fly in a group and can cover thousands of kilometers in a single flight. While flying, a group of geese forms themselves as a Vconfiguration. In this way, the geese in the front can minimize the air resistance of the ones in the back. This allows the geese to fly around 70% farther as a group than they could individually. The GGO algorithm is first validated by being applied to nineteen datasets retrieved from the UCI Machine Learning Repository. Each dataset contains a varied amount of characteristics, instances, and classes that are used to choose features. After that, it is put to use in the process of solving a number of engineering benchmark functions and case studies. Several case studies are solved using the proposed algorithm too, including the pressure vessel design and the tension/compression spring design. The findings demonstrate that the GGO method outperforms numerous other comparative optimization algorithms and delivers superior accuracy compared to other algorithms. The results of the statistical analysis tests, such as Wilcoxon's rank-sum and one-way analysis of variance (ANOVA), demonstrate that the GGO algorithm achieves superior results.
Keyword:
Swarm-based algorithms
Meta-heuristic
Algorithm
Engineering optimization
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
分享收藏An effective multi-objective artificial hummingbird algorithm with dynamic elimination-based crowding distance for solving engineering design problems一种求解工程设计问题的动态消去拥挤距离多目标人工蜂鸟算法
PREAI
分享收藏分享收藏Multi-strategy ensemble grey wolf optimizer and its application to feature selection多策略集成灰狼优化器及其在特征选择中的应用
PREAI
分享收藏Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容