Regression models using pattern search assisted least square support vector machines - Patil, N. S.; Shelokar, P. S.; Jayaraman, Valadi K.; Kulkarni, B. D. (Chemical Engineering Research and Design, 2005)
Least Square Support Vector Machines (LS-SVM), a new machine-learning tool has been employed for developing data driven models of non-linear processes. The method is firmly rooted in the statistical learning theory and transforms the input data to a higher dimensional feature space where the use of appropriate kernel functions avoid computational difficulty. Further, a pattern search algorithm, which explores multiple directions and utilizes coordinate search with fixed step size, is employed for selecting optimal LS-SVM model that produces a minimum possible prediction error. To show the efficacy and efficiency of the fully automated pattern search assisted LS-SVM methodology, we have tested it on several benchmark examples. The study suggests that proposed paradigm can be a useful and viable tool in building data driven models of non-linear processes.
Keywords: equality constraints; LS-SVM; model selection; Optimization; pattern search

Multicanonical jump walk annealing assisted by tabu for dynamic optimization of chemical engineering processes - Shelokar, P. S.; Jayaraman, Valadi K.; Kulkarni, B. D. (European Journal of Operational Research, 2008)
A hybrid methodology, viz., multicanonical jump walk annealing assisted by tabu list (MJWAT) is proposed for solving dynamic optimization problems in chemically reacting systems. This method combines the power of multicanonical sampling with the beneficial features of simulated annealing. Incorporating tabu list further enhances the efficiency of the method. The superior performance of the MJWAT is highlighted with the help of five benchmark case studies. (C) 2006 Elsevier B.V. All rights reserved.
Keywords: dynamic optimization; metaheuristics; Monte Carlo simulations; multicanonical algorithms; simulated annealing; tabu conditions