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However, unlike local post-processing optimizers, our method aims at a more global optimization of the initial path, if possible, performing a drastic topological update, but instead of re-planning in the whole robot configuration space (C), it uses the initial path to limit the search within an optimal subset of C. Should a better solution be found, the subspace is further reduced.
Coupled with multidisciplinary and trans disciplinary teams, this less familiar and less widely used approach would offer a more global optimization.
The loop decoy set and the CASP8 targets are examples of decoy sets for which such a more global optimization of full-atom models appears to have been achieved, and, consequently, they appear more difficult both with respect to the van der Waals term and the overall all-atom scoring function.
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Modern metaheuristic algorithms are generally more suitable for global optimization.
Compared with traditional PSO algorithm, the APSO algorithm is more efficient in searching global optimization solution and the details of APSO algorithm is presented in literature [23].
In order to improve the efficiency of PS and obtain a more powerful algorithm for global optimization, a new algorithm named Free Pattern Search (FPS) based on PS and Free Search FSS) is proposed in this paper.
To run multimodal optimization algorithm, we must track more information than the global optimization.
Therefore, traditional local methods may fail and there is a need to use more sophisticated techniques as global optimization (GO) to successfully fit the model.
In order to make the model-based approach more efficient, we propose new global optimization constraints with dedicated filtering algorithms.
GA has the potential to solve global optimization problems more efficiently than many other stochastic inversion techniques.
Hence it is needed to use global optimization methods that provide more guarantees of converging to the globally optimal solution [ 11, 35].
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