Coronavirus Mask Protection Algorithm: A New Bio-inspired Optimization Algorithm and Its Applications

Nowadays, meta-heuristic algorithms are attracting widespread interest in solving high-dimensional nonlinear optimization problems. In this paper, a COVID-19 prevention-inspired bionic optimization algorithm, named Coronavirus Mask Protection Algorithm (CMPA), is proposed based on the virus transmis...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of bionics engineering Ročník 20; číslo 4; s. 1747 - 1765
Hlavní autoři: Yuan, Yongliang, Shen, Qianlong, Wang, Shuo, Ren, Jianji, Yang, Donghao, Yang, Qingkang, Fan, Junkai, Mu, Xiaokai
Médium: Journal Article
Jazyk:angličtina
Vydáno: Singapore Springer Nature Singapore 01.07.2023
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Biochemical Engineering
Bioinformatics
Biomaterials
Biomedical Engineering and Bioengineering
Biomedical Engineering/Biotechnology
Bionics
Coronaviruses
COVID-19
Disease control
Engineering
Gantry cranes
Heuristic methods
Optimization
Parameter identification
Research Article
Bionic algorithm
Optimization algorithm
Metaheuristic algorithm
Parameter identification
Coronavirus Mask Protection Algorithm
Truss optimization
ISSN:1672-6529, 2543-2141
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Nowadays, meta-heuristic algorithms are attracting widespread interest in solving high-dimensional nonlinear optimization problems. In this paper, a COVID-19 prevention-inspired bionic optimization algorithm, named Coronavirus Mask Protection Algorithm (CMPA), is proposed based on the virus transmission of COVID-19. The main inspiration for the CMPA originated from human self-protection behavior against COVID-19. In CMPA, the process of infection and immunity consists of three phases, including the infection stage, diffusion stage, and immune stage. Notably, wearing masks correctly and safe social distancing are two essential factors for humans to protect themselves, which are similar to the exploration and exploitation in optimization algorithms. This study simulates the self-protection behavior mathematically and offers an optimization algorithm. The performance of the proposed CMPA is evaluated and compared to other state-of-the-art metaheuristic optimizers using benchmark functions, CEC2020 suite problems, and three truss design problems. The statistical results demonstrate that the CMPA is more competitive among these state-of-the-art algorithms. Further, the CMPA is performed to identify the parameters of the main girder of a gantry crane. Results show that the mass and deflection of the main girder can be improved by 16.44% and 7.49%, respectively.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1672-6529
2543-2141
DOI:10.1007/s42235-023-00359-5