A comparative study of multi-objective optimization methodologies for molecular and process design

• Published in: Computers & chemical engineering 2020-05, Vol.136, p.106802, Article 106802
• Main Authors: Lee, Ye Seol, Graham, Edward J., Galindo, Amparo, Jackson, George, Adjiman, Claire S.
• Format: Article
• Language: English
• Subjects: Computer-aided molecular and process design; Computer-aided molecular design; Global optimization; Mixed-integer nonlinear programming; Multi-objective optimization
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2020.106802

The need to consider multiple objectives in molecular design, whether based on techno-economic, environmental or health and safety metrics is increasingly recognized. There is, however, limited understanding of the suitability of different multi-objective optimization (MOO) algorithm for the solution of such design problems. In this work, we present a systematic comparison of the performance of five mixed-integer non-linear programming (MINLP) MOO algorithms on the selection of computer-aided molecular design (CAMD) and computer-aided molecular and process design (CAMPD) problems. The five methods are designed to address the discrete and nonlinear nature of the problem, with the aim of generating an accurate approximation of the Pareto front. They include: a weighted sum approach without global search phases (SWS), a weighted sum approach with simulated annealing (WSSA), a weighted sum approach with multi level single linkage (WSML), the sandwich algorithm with MLSL (SDML) and the non dominated sorting genetic algorithm-II (NSGA-II). The algorithms are compared systematically in two steps. The effectiveness of the global search methods is evaluated with SWS, WSSA and WSML. WSML is found to be most effective and a comparative analysis of WSML, SDML and NSGA-II is then undertaken. As a test set of these optimization techniques, two CAMD and one CAMPD problems of varying dimensionality are formulated as case studies. The results show that the SDML provides the most efficient generation of a diverse set of Pareto points, leading to the construction of an approximate Pareto front close to exact Pareto front.