Multi-objective Optimisation Based on Relation Favour

Many optimisation problems in circuit design, in the following also refereed to as VLSI CAD, consist of mutually dependent sub-problems, where the resulting solutions must satisfy several requirements. Recently, a new model for Multi-Objective Optimisation (MOO) for applications in Evolutionary Algorithms (EAs) has been proposed. The search space is partitioned into so-called Satisfiability Classes (SCs), where each region represents the quality of the optimisation criteria. Applying the SCs to individuals in a population a fitness can be assigned during the EA run. The model also allows the handling of infeasible regions and restrictions in the search space. Additionally, different priorities for optimisation objectives can be modelled. In this paper, the model is studied in further detail. Various properties are shown and advantages and disadvantages are discussed. The relations to other techniques are presented and experimental results are given to demonstrate the efficiency of the model.