Distributed fitness landscape analysis for cooperative search with domain decomposition

Fitness landscape analysis is often employed to quantify the properties of optimization problems and hence gain a better understanding of these problems and the behavior of the algorithms applied to them. The calculation of various landscape features requires complete knowledge of the boundaries and constraints of the entire search space. Many real-world applications of distributed optimization exhibit an inherent domain decomposition, i.e., the decision variables for a cooperative search are in the hands of multiple actors. Thus, knowledge about the overall search space - likewise distributed - is not available at a central location. In this paper, we propose an approach for distributed computation and subsequent composition of fitness landscape features. We evaluate the approach with a set of well-known continuous benchmark functions and examine the features for correlation with algorithm performance and their suitability for feature-based algorithm parameterization. The results show that the distributedly computed features provide useful insights into the nature of the problems and that especially the heterogeneity of the sub-search spaces is a relevant factor in the optimized design of the exchange mechanisms of distributed heuristics.