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This work introduces a strategy for optimal design of distillation systems based on continuous optimization.
This work introduces a strategy for the optimal design of distillation systems based on continuous optimization.
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It highlights a range of real-world problems that are both challenging and worthwhile, using models and methods based on continuous and discrete optimization, network optimization, simulation and system dynamics, heuristics, metaheuristics, artificial intelligence, analytics, and multiple-criteria decision making.
In this workshop, we explore the state-of-the-art techniques for performing discrete optimization based on continuous relaxations of the underlying problem, as well as our current understanding of the limitations of this kind of approach.
The initial discrete-valued topology optimization problem is established as an optimization problem based on continuous design variables by employing a material density field into the design domain.
The acoustic field is modeled by Helmholtz equation and the topology optimization method is based on continuous material interpolation functions in the density and bulk modulus.
Self-organization is based on continuous feedback loops.
An obvious first step towards optimization would be to abandon the binarization of the data (which here was used to produce trees that are easier to interpret) and build a GP based on continuous expression values.
This paper introduces a new stochastic approach for assisted history matching based on a continuous ant colony optimization algorithm.
Then, an optimization approach based on the ordinal optimization (OO) philosophy and particle swarm optimization (PSO) is used to search in the continuous space of the operational variables.
In "Robust optimization", scenario-based robust approach based on Mulvey robust optimization has been expressed briefly.
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