.png){kind=icon}
返回
The Bedbug Meta-heuristic Algorithm to Solve Optimization Problems
.png){kind=icon}
PRE
AI
DOI：10.1007/s42235-023-00356-8.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Small parasitic Hemipteran insects known as bedbugs (Cimicidae) feed on warm-blooded mammal's blood. The most famous member of this family is the Cimex lectularius or common bedbug. The current paper proposes a novel swarm intelligence optimization algorithm called the Bedbug Meta-Heuristic Algorithm (BMHA). The primary inspiration for the bedbug algorithm comes from the static and dynamic swarming behaviors of bedbugs in nature. The two main stages of optimization algorithms, exploration, and exploitation, are designed by modeling bedbug social interaction to search for food. The proposed algorithm is benchmarked qualitatively and quantitatively using many test functions including CEC2019. The results of evaluating BMHA prove that this algorithm can improve the initial random population for a given optimization problem to converge towards global optimization and provide highly competitive results compared to other well-known optimization algorithms. The results also prove the new algorithm's performance in solving real optimization problems in unknown search spaces. To achieve this, the proposed algorithm has been used to select the features of fake news in a semi-supervised manner, the results of which show the good performance of the proposed algorithm in solving problems.
Keyword:
Bedbug Meta-Heuristic Algorithm
Optimization algorithm
BMHA
期刊
IF:
5.8
论文数:
2.0K
被引数:
4.8K
机构
引用论文
.png){kind=icon}
Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm蛾焰优化算法: 一种新的自然启发启发式范式
PREAI
分享收藏分享收藏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
查看更多内容