Comparison of Data Structures for Storing Pareto-sets in MOEAs

In MOEAs with elitism, the data structures and algorithms for storing and updating archives may have a great impact on the CPU time, especially when optimizing continuous problems with larger population sizes. In this paper, we introduce Quad-trees as an efficient data structure for storing Pareto-points. Apart from conventional linear lists, we have implemented three kinds of Quad-trees for the archives. These data structures were examined for different examples. The results presented show that linear lists perform better in terms of CPU time for small population sizes whereas tree structures perform better for large population sizes.