Govindan, Deepak ; Chakraborty, Suman ; Chakraborti, Nirupam (2010) Analyzing the fluid flow in continuous casting through evolutionary neural nets and multi-objective genetic algorithms Steel Research International, 81 (3). pp. 197-203. ISSN 1611-3683
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Official URL: http://onlinelibrary.wiley.com/doi/10.1002/srin.20...
Related URL: http://dx.doi.org/10.1002/srin.200900128
Abstract
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™ 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 modeFRONTIER™ and the results are compared and analysed.
| Item Type: | Article |
|---|---|
| Source: | Copyright of this article belongs to John Wiley and Sons. |
| ID Code: | 100894 |
| Deposited On: | 04 Jan 2017 11:56 |
| Last Modified: | 04 Jan 2017 11:56 |
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