Studies in adaptive random search optimization for MINLP problems

Abstract

Stochastic optimization approaches are achieving growing interest in application to difficult optimization problems common in chemical and process engineering. They are easy to use especially in cases where there are disjunctive functions and logical conditions. M-LJ optimization algorithm from adaptive random search (ARS) strategy has been developed for most advantageous optimization problems, i.e. MINLP ones. This method is an extension of LJ algorithm developed by Jaakola and Luus (1974) for problems with continuous variables. The main modification in M-LJ method is the change of distribution function for integer variables. The results of tests are presented and the comparison with the performance of other stochastic methods as e.g. M-SIMPSA algorithm from Cardoso et al. (1997). M-LJ has shown high robustness and can be seen easy to use optimization tool for certain MINLP problems. Also, the re-formulation of difficult optimization MINLP problem for batch processes from Kocis and Grossmann (1988) has been developed which allows for solving it as NLP task.