title: Modified particle swarm optimization algorithm for variable selection in MLR and PLS modeling: QSAR studies of antagonism of angiotensin II antagonists.
abstract: A version of modified particle swarm optimization (PSO) algorithm has been proposed. The PSO algorithm has been modified to adopt to the discrete combinatorial optimization problem and reduce the probability of sinking into local optima. In the modified PSO algorithm, the velocity represents the probability of element in each particle taking value 1 or 0. The modified discrete PSO algorithm is proposed to select variables in MLR and PLS modeling and to predict antagonism of angiotensin II antagonists. The modified C(p) is employed as fitness function. The results were compared to those obtained by GAs. Experimental results have demonstrated that the modified PSO is a useful tool for variable selection which converges quickly towards the optimal position.
authors.affiliation: State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Shen
authors.forename: Jian-Hui
authors.initials: JH
authors.lastname: Jiang
authors.forename: Chen-Xu
authors.initials: CX
authors.lastname: Jiao
authors.forename: Guo-Li
authors.initials: GL
authors.lastname: Shen
authors.forename: Ru-Qin
authors.initials: RQ
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D000804:Angiotensin II / Q000037:antagonists & inhibitors* / Q000737:chemistry*; D016018:Least-Squares Analysis; D016014:Linear Models; D008956:Models, Chemical; D010316:Particle Size; D021281:Quantitative Structure-Activity Relationship
chemical_list: D000804:Angiotensin II