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The transformation method allows to transform the mixed optimization problem into the continuous one.
As p i,j ∈{0,1}, it is a mixed optimization problem combining both integer programming and linear programming, which is difficult to be solved.
Thus, an optimization methodology that can deal with a mixed optimization problem and includes both continuum and discrete design variables is developed.
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Non-linear prediction models, like artificial neural networks (e.g., NetMHC [ 3]), or even more complex prediction approaches like the one proposed by Zhang et al. [ 19], would lead to a non-convex, non-linear mixed integer optimization problem that cannot be solved efficiently and optimally even for small instances [ 20].
The resulting model is a bilevel mixed integer optimization problem.
The combined gas and electricity system planning is a complicated mixed integer optimization problem.
Saraiva et al. (2011) formulated the scheduling problem of generator maintenance actions as a mixed integer optimization problem.
The resource allocation strategy for network-coded 1S2P cooperation is modeled as a mixed integer optimization problem.
A multi-subgroup hierarchical chaos hybrid algorithm is developed to deal with the bi-level mixed integer optimization problem.
The problem of designing such an observer, also called a residual generator, is formulated as a mixed H2/H∞ optimization problem.
Finally, to solve this bi-level mixed integer optimization problem efficiently, a multi-subgroup hierarchical chaos algorithm is introduced based on DE and PSO.
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Justyna Jupowicz-Kozak
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