Abstract
Molecular docking is a complex optimization problem aimed at predicting the position of a ligand molecule in the active site of a receptor with the lowest binding energy. This problem can be formulated as a bi-objective optimization problem by minimizing the binding energy and the Root Mean Square Deviation (RMSD) difference in the coordinates of ligands. In this context, the SMPSO multi-objective swarm-intelligence algorithm has shown a remarkable performance. SMPSO is characterized by having an external archive used to store the non-dominated solutions and also as the basis of the leader selection strategy. In this paper, we analyze several SMPSO variants based on different archiving strategies in the scope of a benchmark of molecular docking instances. Our study reveals that the SMPSOhv, which uses an hypervolume contribution based archive, shows the overall best performance.
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Acknowledgments
This work is partially funded by Grants TIN2011-25840 (Ministerio de Ciencia e Innovación) and P11-TIC-7529 and P12-TIC-1519 (Plan Andaluz I+D+I). This article is based upon work from COST Action CA15140, supported by COST (European Cooperation in Science and Technology).
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García-Nieto, J., López-Camacho, E., Godoy, M.J.G., Nebro, A.J., Durillo, J.J., Aldana-Montes, J.F. (2016). A Study of Archiving Strategies in Multi-objective PSO for Molecular Docking. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2016. Lecture Notes in Computer Science(), vol 9882. Springer, Cham. https://doi.org/10.1007/978-3-319-44427-7_4
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DOI: https://doi.org/10.1007/978-3-319-44427-7_4
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