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For the role assignment problem, we give an 1/2-approximate algorithm to find a nearly optimal solution.
This paper formulates the text feature selection problem as a combinatorial problem and proposes an Ant Colony Optimization (ACO) algorithm to find the nearly optimal solution for the same.
For efficiency, an extensive numerical experiment shows that the proposed method can find the optimal or nearly optimal solution of GRAP under a reasonable computational budget and outperforms the other existing methods on the created scenarios.
Therefore, this is a nearly optimal solution to the integral bit loading requirement.
In the following, we compare the performance of the distributed algorithm to an (nearly) optimal solution based on BNB.
In addition, the proposed algorithm is shown to be convergent to a nearly optimal solution of the problem.
Similar(52)
Instead of a single approximation of the optimal solution, AIMS-OPT produces a set of nearly optimal solutions where the accuracy of the near-optimality is controlled by the user.
Two branch-and-bound algorithms are described to generate nearly optimal solutions, and methods are discussed to summarise and to present nearly optimal solutions.
Experiments reveal that the proposed algorithm can efficiently yield nearly optimal solutions against stochastic demands.
This case study shows that the presentation of nearly optimal solutions provides relevant information to resolve complex decision problems.
Optimization algorithms are frequently designed for NP-hard problems to find nearly optimal solutions with a practical time complexity.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com