Solving DCOPs with Distributed Large Neighborhood Search
Ferdinando Fioretto, Agostino Dovier, Enrico Pontelli and, William Yeoh, Roie Zivan
TL;DR
This paper introduces a Distributed Large Neighborhood Search framework for DCOPs that offers solution quality guarantees and leverages problem structure, outperforming existing incomplete algorithms in large-scale multi-agent optimization tasks.
Contribution
The paper presents a novel D-LNS framework with a repair phase that guarantees solution quality and exploits problem structure, advancing incomplete DCOP solving methods.
Findings
D-LNS outperforms existing incomplete algorithms on various problem instances.
The framework provides bounds refinement during the iterative process.
It effectively exploits domain-dependent knowledge and problem structure.
Abstract
The field of Distributed Constraint Optimization has gained momentum in recent years, thanks to its ability to address various applications related to multi-agent cooperation. Nevertheless, solving Distributed Constraint Optimization Problems (DCOPs) optimally is NP-hard. Therefore, in large-scale, complex applications, incomplete DCOP algorithms are necessary. Current incomplete DCOP algorithms suffer of one or more of the following limitations: they (a) find local minima without providing quality guarantees; (b) provide loose quality assessment; or (c) are unable to benefit from the structure of the problem, such as domain-dependent knowledge and hard constraints. Therefore, capitalizing on strategies from the centralized constraint solving community, we propose a Distributed Large Neighborhood Search (D-LNS) framework to solve DCOPs. The proposed framework (with its novel repair…
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Taxonomy
TopicsConstraint Satisfaction and Optimization · Vehicle Routing Optimization Methods · Scheduling and Timetabling Solutions