title: Hierarchical particle swarm optimizer for minimizing the non-convex potential energy of molecular structure.
abstract: 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.
authors.affiliation: Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, 800 Dongchuan Road, Shanghai 200240, China.
authors.forename: Ngaam J
authors.initials: NJ
authors.lastname: Cheung
authors.affiliation: Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, 800 Dongchuan Road, Shanghai 200240, China. Electronic address: hbshen@sjtu.edu.cn.
authors.forename: Hong-Bin
authors.initials: HB
authors.lastname: Shen
mesh_terms: D000465:Algorithms; D008968:Molecular Conformation; D015394:Molecular Structure
keywords: Heterogeneous search; Hierarchical group; Molecular conformation; Particle swarm optimization; Swarm migration