Abstract
Solving Distributed Constraint Optimization Problems has a large significance in today’s interconnected world. Complete as well as approximate algorithms have been discussed in the relevant literature. However, these are unfeasible if high-arity constraints are present (i.e., a fully connected constraint graph). This is the case in distributed combinatorial problems, for example in the provisioning of active power in the domain of electrical energy generation. The aim of this paper is to give a detailed formalization and evaluation of the COHDA heuristic for solving these types of problems. The heuristic uses self-organizing mechanisms to optimize a common global objective in a fully decentralized manner. We show that COHDA is a very efficient decentralized heuristic that is able to tackle a distributed combinatorial problem, without being dependent on centrally gathered knowledge.
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Acknowledgments
Due to the vast amounts of simulations needed, all experiments have been conducted on HERO, a multi-purpose cluster installed at the University of Oldenburg, Germany. We would like to thank the maintenance team from HERO for their valuable service. We also thank Ontje Lünsdorf for providing the asynchronous message passing framework used in our simulation environment, and Jörg Bremer for providing the CHP simulation model.
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Hinrichs, C., Lehnhoff, S., Sonnenschein, M. (2014). COHDA: A Combinatorial Optimization Heuristic for Distributed Agents. In: Filipe, J., Fred, A. (eds) Agents and Artificial Intelligence. ICAART 2013. Communications in Computer and Information Science, vol 449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44440-5_2
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DOI: https://doi.org/10.1007/978-3-662-44440-5_2
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