A Study of Archiving Strategies in Multi-objective PSO for Molecular Docking

被引：5

作者：

Garcia-Nieto, Jose ^{[1
]}

Lopez-Camacho, Esteban ^{[1
]}

Garcia Godoy, Maria Jesus ^{[1
]}

Nebro, Antonio J. ^{[1
]}

Durillo, Juan J. ^{[2
]}

Aldana-Montes, Jose F. ^{[1
]}

机构：

[1] Univ Malaga, ETSI Informat, Dept Comp Sci, Khaos Res Grp, Campus Teatinos, Malaga, Spain

[2] Univ Innsbruck, Distributed & Parallel Syst Grp, Innsbruck, Austria

来源：

SWARM INTELLIGENCE | 2016年 / 9882卷

关键词：

Multi-objective optimization; Particle Swarm Optimization; Molecular docking; Archiving strategies; Algorithm comparison; OPTIMIZERS; ALGORITHM;

D O I：

10.1007/978-3-319-44427-7_4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：40 / 52

页数：13

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