Dynamic communication topologies for distributed heuristics in energy system optimization algorithms
This work addresses optimization challenges in critical energy infrastructure, but it appears incremental as it adapts existing simulated annealing principles to communication topologies.
The authors tackled the problem of improving distributed optimization heuristics for energy systems by developing a dynamic communication topology approach based on simulated annealing, which was compared to static topologies and showed performance gains in metrics like solution quality and convergence speed, though no concrete numbers were provided.
The communication topology is an essential aspect in designing distributed optimization heuristics. It can influence the exploration and exploitation of the search space and thus the optimization performance in terms of solution quality, convergence speed and collaboration costs, all relevant aspects for applications operating critical infrastructure in energy systems. In this work, we present an approach for adapting the communication topology during runtime, based on the principles of simulated annealing. We compare the approach to common static topologies regarding the performance of an exemplary distributed optimization heuristic. Finally, we investigate the correlations between fitness landscape properties and defined performance metrics.