Improving Solution Quality of Bounded Max-Sum Algorithm to Solve DCOPs involving Hard and Soft Constraints

TL;DR

This paper enhances the Bounded Max-Sum algorithm to effectively handle DCOPs with both hard and soft constraints, leading to significantly better solution quality in large-scale problems.

Contribution

The authors adapt BMS to incorporate hard constraints, improving solution quality for DCOPs with mixed constraint types.

Findings

Marked improvement in solution quality for large DCOPs

Effective handling of both hard and soft constraints

Empirical validation demonstrating enhanced performance

Abstract

Bounded Max-Sum (BMS) is a message-passing algorithm that provides approximation solution to a specific form of de-centralized coordination problems, namely Distributed Constrained Optimization Problems (DCOPs). In particular, BMS algorithm is able to solve problems of this type having large search space at the expense of low computational cost. Notably, the traditional DCOP formulation does not consider those constraints that must be satisfied(also known as hard constraints), rather it concentrates only on soft constraints. Hence, although the presence of both types of constraints are observed in a number of real-world applications, the BMS algorithm does not actively capitalize on the hard constraints. To address this issue, we tailor BMS in such a way that can deal with DCOPs having both type constraints. In so doing, our approach improves the solution quality of the algorithm. The empirical results exhibit a marked improvement in the quality of the solutions of large DCOPs.