Local Search Based Approximate Algorithm for Multi-Objective DCOPs

Abstract

Many real world optimization problems involve multiple criteria that should be considered separately and optimized simultaneously. A Multi-Objective Distributed Constraint Optimization Problem (MO-DCOP) is the extension of a mono-objective Distributed Constraint Optimization Problem (DCOP). A DCOP is a fundamental problem that can formalize various applications related to multi-agent cooperation. Solving an MO-DCOP is to find the Pareto front which is a set of cost vectors obtained by Pareto optimal solutions. In MO-DCOPs, even if a constraint graph has the simplest tree structure, the size of the Pareto front (the number of Pareto optimal solutions) is often exponential in the number of agents. Since finding all Pareto optimal solutions becomes easily intractable, it is important to consider fast but approximate algorithms. Various sophisticated algorithms have been developed for solving a DCOP and an MO-COP. However, there exists few works on an MO-DCOP. The Bounded Multi-Objective Max-Sum (B-MOMS) algorithm is the first and only existing approximate MO-DCOP algorithm. In this paper, we develop a novel approximate MO-DCOP algorithm called Distributed Iterated Pareto Local Search (DIPLS) and empirically show that DIPLS outperforms the state-of-the-art B-MOMS algorithm.