Generalized uncertainty applied to optimization

Abstract

For the computational component of dynamical systems we wish to implement advanced algorithms based on fuzzy logic combined with differential equations so that real-time differential constraints can be obtained which will be important for Dengue fever response. Computational Methods for Control: We plan to develop algorithms to solve optimal control problems under generalized uncertainty. We begin by extending the work of Lodwick (1980) and then employ evolutionary algorithms such as Gen D. If we are successful, it will enable us to use our methods with realistic real-time data associated with our application. As regards Computational Methods for Generalized Uncertainty Optimization models, as above, we plan to look at the work of Buscena (2004) and develop a class of evolutionary algorithm based on participatory learning and selective transfer to solve static and dynamic uncertain optimization models. (AU)