A Particle Swarm Based Algorithm for Functional Distributed Constraint Optimization Problems

TL;DR

This paper introduces PFD, a novel particle swarm optimization-based algorithm for functional distributed constraint optimization problems that reduces computational and memory overhead while improving solution quality.

Contribution

The paper presents a new PSO-inspired distributed algorithm for F-DCOPs, addressing high resource consumption of existing methods and proving its anytime property.

Findings

PFD outperforms existing approaches in solution quality.

PFD significantly reduces computation and memory requirements.

Theoretical proof that PFD is an anytime algorithm.

Abstract

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.