  Distributed Constraint Optimization Problems (DCOPs) are a widely studied
constraint handling framework. The objective of a DCOP algorithm is to optimize
a global objective function that can be described as the aggregation of a
number of distributed constraint cost functions. In a DCOP, each of these
functions is defined by a set of discrete variables. However, in many
applications, such as target tracking or sleep scheduling in sensor networks,
continuous valued variables are more suited than the discrete ones. Considering
this, Functional DCOPs (F-DCOPs) have been proposed that is able to explicitly
model a problem containing continuous variables. Nevertheless, the
state-of-the-art F-DCOPs approaches experience onerous memory or computation
overhead. To address this issue, we propose a new F-DCOP algorithm, namely
Particle Swarm Based F-DCOP (PFD), which is inspired by a meta-heuristic,
Particle Swarm Optimization (PSO). Although it has been successfully applied to
many continuous optimization problems, the potential of PSO has not been
utilized in F-DCOPs. To be exact, PFD devises a distributed method of solution
construction while significantly reducing the computation and memory
requirements. Moreover, we theoretically prove that PFD is an anytime
algorithm. Finally, our empirical results indicate that PFD outperforms the
state-of-the-art approaches in terms of solution quality and computation
overhead.
