  Distributed Constraint Optimization Problems (DCOPs) are an important
framework for modeling coordinated decision-making problems in multi-agent
systems with a set of discrete variables. Later works have extended DCOPs to
model problems with a set of continuous variables, named Functional DCOPs
(F-DCOPs). In this paper, we combine both of these frameworks into the Mixed
Integer Functional DCOP (MIF-DCOP) framework that can deal with problems
regardless of their variables' type. We then propose a novel algorithm $-$
Distributed Parallel Simulated Annealing (DPSA), where agents cooperatively
learn the optimal parameter configuration for the algorithm while also solving
the given problem using the learned knowledge. Finally, we empirically evaluate
our approach in DCOP, F-DCOP, and MIF-DCOP settings and show that DPSA produces
solutions of significantly better quality than the state-of-the-art non-exact
algorithms in their corresponding settings.
