Imperial College London

Prof Claire S. Adjiman FREng

Faculty of EngineeringDepartment of Chemical Engineering

Professor of Chemical Engineering



+44 (0)20 7594 6638c.adjiman Website




Ms Sevgi Thompson +44 (0)20 7594 1478




608Roderic Hill BuildingSouth Kensington Campus






BibTex format

author = {Lee, L and Graham, E and Galindo, A and Jackson, G and Adjiman, C},
doi = {10.1016/j.compchemeng.2020.106802},
journal = {Computers and Chemical Engineering},
title = {A comparative study of multi-objective optimization methodologies for molecular and process design},
url = {},
volume = {136},
year = {2020}

RIS format (EndNote, RefMan)

AB - 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 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) multi-objective optimization 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 (SA), a weighted sum approach with multi level single linkage (MLSL), the sandwich algorithm with MLSL 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 of CAMD and one CAMPD problems of varying dimensionality are formulated as case studies. The results show that the sandwich algorithm with MLSL 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.
AU - Lee,L
AU - Graham,E
AU - Galindo,A
AU - Jackson,G
AU - Adjiman,C
DO - 10.1016/j.compchemeng.2020.106802
PY - 2020///
SN - 0098-1354
TI - A comparative study of multi-objective optimization methodologies for molecular and process design
T2 - Computers and Chemical Engineering
UR -
UR -
VL - 136
ER -