JACIII Vol.18 No.4 pp. 573-580
doi: 10.20965/jaciii.2014.p0573


A Two-Phase Complete Algorithm for Multi-Objective Distributed Constraint Optimization

Alexandre Medi*, Tenda Okimoto**, and Katsumi Inoue***

*University of Nantes, Direction des Relations Internationales, BP 13522, F-44 035, Nantes cedex 1, France

**Faculty of Maritime Sciences, Kobe University, 5-1-1 Higashinadaku, Fukaeminamimachi, Kobe 658-0022, Japan

***National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyodaku, Tokyo 101-8430, Japan

July 24, 2013
January 31, 2014
July 20, 2014
multi-agent system, distributed constraint optimization, multi-criteria

A Distributed Constraint Optimization Problem (DCOP) is a fundamental problem that can formalize various applications related to multi-agent cooperation. Many application problems in multi-agent systems can be formalized as DCOPs. However, 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 an extension of a mono-objective DCOP. Compared to DCOPs, there exists few works on MO-DCOPs. In this paper, we develop a novel complete algorithm for solving an MO-DCOP. This algorithm utilizes a widely used method called Pareto Local Search (PLS) to generate an approximation of the Pareto front. Then, the obtained information is used to guide the search thresholds in a Branch and Bound algorithm. In the evaluations, we evaluate the runtime of our algorithm and show empirically that using a Pareto front approximation obtained by a PLS algorithm allows to significantly speed-up the search in a Branch and Bound algorithm.

Cite this article as:
Alexandre Medi, Tenda Okimoto, and Katsumi Inoue, “A Two-Phase Complete Algorithm for Multi-Objective Distributed Constraint Optimization,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.4, pp. 573-580, 2014.
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