Hierarchical particle swarm optimizer for minimizing the non-convex potential energy of molecular structure

被引:10
作者
Cheung, Ngaarn J. [1 ]
Shen, Hong-Bin [1 ]
机构
[1] Shanghai Jiao Tong Univ, Inst Image Proc & Pattern Recognit, Minist Educ China, Key Lab Syst Control & Informat Proc, Shanghai 200240, Peoples R China
基金
中国国家自然科学基金;
关键词
Particle swarm optimization; Molecular conformation; Heterogeneous search; Hierarchical group; Swarm migration; GLOBAL OPTIMIZATION; CONVERGENCE; ALGORITHM; MINIMIZATION; PREDICTION;
D O I
10.1016/j.jmgm.2014.10.002
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
The stable conformation of a molecule is greatly important to uncover the secret of its properties and functions. Generally, the conformation of a molecule will be the most stable when it is of the minimum potential energy. Accordingly, the determination of the conformation can be solved in the optimization framework. It is, however, not an easy task to achieve the only conformation with the lowest energy among all the potential ones because of the high complexity of the energy landscape and the exponential computation increasing with molecular-size. In this-paper, we develop a hierarchical and heterogeneous particle swarm optimizer (HHPSO) to deal with the problem in the minimization of the potential energy. The proposed method is evaluated over a scalable simplified molecular potential energy function with up to 200 degrees of freedom and a realistic energy function of pseudo-ethane molecule. The experimental results are compared with other six PSO variants and four genetic algorithms. The results show HHPSO is significantly better than the compared PSOs with p-value less than 0.01277 over molecular potential energy function. (C) 2014 Elsevier Inc. All rights reserved.
引用
收藏
页码:114 / 122
页数:9
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