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We summarize the relationship between the various robustness concepts in Figure 6. Figure 6 Scheme of solutions to an uncertain multi-objective optimization problem.
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The proposed method (named GeneSelect) consists of three parts which are fully cooperated: an efficient encoding scheme of candidate solutions, a generalized fitness function, and an intelligent genetic algorithm (IGA).
Open image in new window Fig. 1 Representation scheme of the solution.
The computation complexity and the efficiency of the proposed scheme of exact solution can be illustrated by the following calculations.
After presumption of constituent modes, they are superposed by using a standard finite difference scheme for solution of the classical wave equation.
First, a numerical scheme for solution of high dimensional population balance equations is proposed.
Now, we give the first theorem for a classification scheme of eventually positive solutions to (1).
Second, we present an approach based on simulated annealing that uses the same representation scheme of a feasible solution.
In 1997, Combettes and Hirstoaga [5] introduced an iterative scheme of finding a solution of the problem (1.1) under the assumption that (operatorname {EP}(F)) is nonempty.
According to the results discussed above, we outlined the schemes of electrospinning solution preparation and sustained-release process.
This model is studied through the use of recently developed and benchmarked spectral schemes for the simulation of solutions to stochastic partial differential equations.
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