Journal
STEEL RESEARCH INTERNATIONAL
Volume 81, Issue 3, Pages 197-203Publisher
WILEY-BLACKWELL
DOI: 10.1002/srin.200900128
Keywords
Genetic Algorithms; Continuous Casting; Evolutionary Neural Networks; Evolutionary Computation; Predator-Prey Genetic Algorithm; Multi-objective optimization
Categories
Ask authors/readers for more resources
The flow fields computed for a typical continuous caster are analysed using the basic concepts of Pareto-optimality in the context of multi-objective optimization. The data generated by the flow solver FLUENT (TM) are trained through Evolutionary Neural Networks that emerged through a Pareto-tradeoff between the complexity of the network and its accuracy of training. A number of objectives constructed this way are subjected to optimization using a Multi-objective Predator-Prey Genetic Algorithm. The procedure is repeated using the software mode-FRONTIER (TM) and the results are compared and analysed.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available