DAAD German Australia Research Kooperation

Optimization in presence of multiple conflicting criteria is a problem encountered in several practical domains such as engineering design, scheduling, logistics, finance etc. Such problems are called multi-objective optimization problems (MOP), and their optimum comprises not one but a set of best trade-off solutions known as the Pareto optimal front (POF). There are two key pursuits in solving MOP - first is to search for the POF, and second is to effectively choose design(s) from the POF for implementation. Both these aspects are particularly intractable and computationally prohibitive if the number of objectives is more than three. The existing methods to solve MOP are so called decomposition based evolutionary algorithms (DBEA), which try to solve it by evolving a population of solutions along a pre-defined set of reference vectors. However, defining this set of reference vectors is the biggest challenge for contemporary DBEAs. This project aims to resolve this issue by developing means to quantitatively identify solutions of interest during the search and use them to construct guiding reference vectors for the algorithm. This will enable search for high quality solutions with low computational expense, while also aiding decision making. These two aspects will make the algorithm viable for industrial use.