Volume 16, Issue 3 (7-2026)
IJOCE 2026, 16(3): 325-336 |
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Mahdavi V R, Kaveh A. PREDICTION OF MODAL STRAIN ENERGY USING MULTI-OBJECTIVE METAHEURISTIC-TRAINED ARTIFICIAL NEURAL NETWORKS. IJOCE 2026; 16 (3) :325-336
URL: http://ijoce.iust.ac.ir/article-1-680-en.html
URL: http://ijoce.iust.ac.ir/article-1-680-en.html
1- Department of Civil and Geomechanics Engineering, Arak University of Technology, Arak, Iran
2- School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran-16, Iran
2- School of Civil Engineering, Iran University of Science and Technology, Narmak, Tehran-16, Iran
Abstract: (466 Views)
This paper uses multi-objective methods to improve the exploration for single-objective problems. This method involved splitting the objective function into two segments and these enhanced using specialized algorithms designed for handling multiple objective functions. Three MO algorithms include Colliding Bodies Optimization (MOCBO), Particle Swarm Optimization (MOPSO), and non-dominated sorting genetic algorithm (NSGA-II), which are used to get the best prediction of structural modal strain energy. The indicated method is then implemented to two spatial truss structures. The recently developed method has superior performance over the previous approach that relied on single-objective optimization (SO) algorithms.
Keywords: Neural networks, Strain energy, Optimization algorithms, Multi-objective optimizations, Truss structures
Type of Study: Research |
Subject:
Applications
Received: 2026/05/20 | Accepted: 2026/07/13 | Published: 2026/07/14
Received: 2026/05/20 | Accepted: 2026/07/13 | Published: 2026/07/14
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