The multistart clustering global optimization method called GLOBAL has been introduced in the 1980s for bound constrained global optimization problems with black-box type objective function. Since then the technological environment has been changed much. The present paper describes shortly the revisions and updates made on the involved algorithms to utilize the novel technologies, and to improve its reliability. We discuss in detail the results of the numerical comparison with the old version and with C-GRASP, a continuous version of the GRASP method. According to these findings, the new version of GLOBAL is both more reliable and more efficient than the old one, and it compares favorably with C-GRASP too.

a b s t r a c tThe optimization of thermal processing of foods is a topic which has received great attention during the last decades. The majority of the authors have considered the use of single-objective (criteria) for the optimization. However, the simultaneous optimization of several objectives is much more realistic and desirable, but the associated non-linear programming problems can be very challenging to solve. Here, we describe an efficient and robust multi-criteria optimization method which can be successfully applied to large dynamic systems, like those arising from the modelling of thermal processing of foods. Further, their capabilities for better design and operation of these processes will be highlighted with selected case studies, where the generated Pareto solutions will be analysed. Finally, we will also illustrate their advantages over other recently proposed strategies.

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.