Hybrid DCOP Solvers: Boosting Performance of Local Search Algorithms

TL;DR

This paper introduces a hybrid approach for DCOP solvers that uses greedy initializations to significantly improve convergence speed, reduce communication, and enhance solution quality in distributed optimization problems.

Contribution

It presents a novel initialization method for DCOP solvers that outperforms random starts, leading to faster convergence and better solutions.

Findings

Convergence time reduced by up to 50%

Communication overhead decreased

Solution quality improved

Abstract

We propose a novel method for expediting both symmetric and asymmetric Distributed Constraint Optimization Problem (DCOP) solvers. The core idea is based on initializing DCOP solvers with greedy fast non-iterative DCOP solvers. This is contrary to existing methods where initialization is always achieved using a random value assignment. We empirically show that changing the starting conditions of existing DCOP solvers not only reduces the algorithm convergence time by up to 50\%, but also reduces the communication overhead and leads to a better solution quality. We show that this effect is due to structural improvements in the variable assignment, which is caused by the spreading pattern of DCOP algorithm activation.) /Subject (Hybrid DCOPs)