Result: Optimising a shaft’s geometry by applying genetic algorithms

Title:
Optimising a shaft’s geometry by applying genetic algorithms
Authors:
María Alejandra Guzmán, Alberto Delgado
Source:
Ingeniería e Investigación, Vol 25, Iss 2, Pp 15-23 (2005)
Publisher Information:
Universidad Nacional de Colombia, 2005.
Publication Year:
2005
Subject Terms:
multiobjective optimisation, Optimal design, Multi-material Optimization, FOS: Mechanical engineering, Surrogate Modeling, Engineering, Evolutionary algorithm, Machine learning, FOS: Mathematics, Deflection (physics), Civil and Structural Engineering, Multi-Objective Optimization, Mechanical Engineering, Genetic Algorithms, Sorting, Physics, Mathematical optimization, Topology Optimization in Structural Engineering, Optics, Engineering (General). Civil engineering (General), Computer science, Dynamics and Faults in Gear Systems, Multi-objective optimization, Algorithm, Computational Theory and Mathematics, Genetic algorithm, Particle Swarm Optimization, generic algorithms, mechanical design, Computer Science, Physical Sciences, shafts, TA1-2040, Multiobjective Optimization in Evolutionary Algorithms, Mathematics
Document Type:
Academic journal Article<br />Other literature type
ISSN:
2248-8723
0120-5609
DOI:
10.15446/ing.investig.v25n2.14631
DOI:
10.60692/m2vez-zxw74
DOI:
10.60692/gjtq8-jcd52
Access URL:
https://doaj.org/article/1f601af2b11548df9dd73004f2cd9f69
http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-56092005000200002&lng=pt
https://dialnet.unirioja.es/servlet/articulo?codigo=4902430
https://dialnet.unirioja.es/descarga/articulo/4902430.pdf
https://www.revistas.unal.edu.co/index.php/ingeinv/article/view/14631
http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-56092005000200002
Rights:
CC BY
Accession Number:
edsair.doi.dedup.....8d8f24bb0f05e16fb91f0b35b73ff55f
Database:
OpenAIRE

Further Information

Many engineering design tasks involve optimizing several conflicting goals; these types of problem are known as Multiobjective Optimization Problems (MOPs). Evolutionary techniques have proved to be an effective tool for finding solutions to these MOPs during the last decade. Variations on the basic genetic algorithm have been particularly proposed by different researchers for finding rapid optimal solutions to MOPs. The NSGA (Non-dominated Sorting Genetic Algorithm) has been implemented in this paper for finding an optimal design for a shaft subjected to cyclic loads, the conflicting goals being minimum weight and minimum lateral deflection.