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Distributed Constraint Optimization Problems (DCOPs) are an importantframework for modeling coordinated decision-making problems in multi-agentsystems with a set of discrete variables. Later works have extended DCOPs tomodel problems with a set of continuous variables, named Functional DCOPs(F-DCOPs). In this paper, we combine both of these frameworks into the MixedInteger Functional DCOP (MIF-DCOP) framework that can deal with problemsregardless of their variables' type. We then propose a novel algorithm $-$Distributed Parallel Simulated Annealing (DPSA), where agents cooperativelylearn the optimal parameter configuration for the algorithm while also solvingthe given problem using the learned knowledge. Finally, we empirically evaluateour approach in DCOP, F-DCOP, and MIF-DCOP settings and show that DPSA producessolutions of significantly better quality than the state-of-the-art non-exactalgorithms in their corresponding settings.

Learning Optimal Temperature Region for Solving Mixed Integer Functional DCOPs