Extensions of a Multistart Clustering Algorithm for Constrained Global Optimization Problems

Here, we consider the solution of constrained global optimization problems, such as those arising from the fields of chemical and biosystems engineering. These problems are frequently formulated (or can be transformed to) nonlinear programming problems (NLPs) subject to differential-algebraic equations (DAEs). In this work, we extend a popular multistart clustering algorithm for solving these problems, incorporating new key features including an efficient mechanism for handling constraints and a robust derivative-free local solver. The performance of this new method is evaluated by solving a collection of test problems, including several challenging case studies from the (bio)process engineering area.