Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Computer
Practical genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Structural optimization based on CAD-CAE integration and metamodeling techniques
Computer-Aided Design
ECCE'10/ECCIE'10/ECME'10/ECC'10 Proceedings of the European conference of chemical engineering, and European conference of civil engineering, and European conference of mechanical engineering, and European conference on Control
Review: Structural design employing a sequential approximation optimization approach
Computers and Structures
| Hi-index | 0.00 |
Optimization of mechanical components is an important aspect of the engineering process; a well designed system will lead to money saving during the production phase and better machine life. On the other hand, optimization actions will increase the engineering investment. Consequently, and since computer time is inexpensive, an efficient design strategy will tend to transfer the effort from the staff to the computers. This paper presents an efficient design tool made to carry out this task: a new optimization model based on genetic algorithms is developed to work with commercial finite element software. The objective is to automate optimization of static criteria (stresses, weight, strength, etc.) with finite element models. In the proposed model, the process acts on two geometric aspects of the shape to be optimized: it controls the position of the vertices defining the edges of the volume and, in order to minimize stresses concentrations, it can add and define fillet between surfaces. The model is validated from some benchmark tests. An industrial application is presented: the genetic algorithms-finite element model is employed to design the fillets at the crown-blade junctions of a hydroelectric turbine. The results show that the model converges to a very efficient solution without any engineer intervention.