On the assessment of multiobjective approaches to the adaptive distributed database management problem

In this paper we assess the performance of three modern multiobjective evolutionary algorithms on a real-world optimization problem related to the management of distributed databases. The algorithms assessed are the Strength Pareto Evolutionary Algorithm (SPEA), the Pareto Archived Evolution Strategy (PAES), and M-PAES, which is a Memetic Algorithm based variant of PAES. The performance of these algorithms is compared using two distinct and sophisticated multiobjective-performance comparison techniques, and extensions to these comparison techniques are proposed. The information provided by the different performance assessment techniques is compared, and we find that, to some extent, the ranking of algorithm performance alters according to the comparison metric; however, it is possible to understand these differences in terms of the complex nature of multiobjective comparisons.