Abstract

A metaheuristic based on scatter search for global dynamic optimization of chemical and bio-chemical processes is presented. It is designed to overcome typical difficulties of nonlinear dynamic systems optimization such as noise, flat areas, non-smoothness and/or discontinuities. It balances between intensification and diversification by coupling a local search procedure with a global search and makes use of memory to avoid simulations in previously explored areas. Its application to three dynamic optimization case studies proves its efficiency and robustness, showing also a very good scalability.