Optimizing ternary hybrid nanofluids using neural networks, gene expression programming, and multi-objective particle swarm optimization: a computational intelligence strategy
INTI International University · Guizhou University · +12 more institutions
Abstract
The performance of nanofluids is largely determined by their thermophysical properties. Optimizing these properties can significantly enhance nanofluid performance. This study introduces a hybrid strategy based on computational intelligence to determine the optimal conditions for ternary hybrid nanofluids. The goal is to minimize dynamic viscosity and maximize thermal conductivity by varying the volume fraction, temperature, and nanomaterial mixing ratio. The proposed strategy integrates machine learning, multi-objective optimization, and multi-criteria decision-making. Three machine learning techniques-GMDH-type neural network, gene expression programming, and combinatorial algorithm-are applied to model…
Citation impact
- FWCI
- 38.18
- Percentile
- 100%
- References
- 49
Authors
10- THTao Hai
INTI International University, Guizhou University, Ajman University, City University Ajman, Qiannan Normal College For Nationalities
- ABAli Basem
University of Kerbala, University of Warith Al-Anbiyaa
- AAAs’ad Alizadeh
Cihan University-Erbil
- PKPradeep Kumar Singh
GLA University
- HRHusam Rajab
Najran University
Topics & keywords
- Particle swarm optimization
- Gene expression programming
- Artificial neural network
- Computer science
- Ternary operation
- Genetic programming
- Swarm intelligence
- Artificial intelligence