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.
No takes yet. Share an insight, caveat, or question.
Govindan et al. (2010) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: