Whenever multiple stakeholders try to optimize a common objective function in a distributed way, an adroit coordination mechanism is necessary. This contribution presents a formal model of distributed combinatorial optimization problems. Subsequently, a heuristic is introduced, that uses self-organizing mechanisms to optimize a common global objective as well as individual local objectives in a fully decentralized manner. This heuristic, COHDA 2 , is implemented in an asynchronous multi-agent system, and is being extensively evaluated by means of a real-world problem from the smart grid domain. We give insight into the convergence process and show the robustness of COHDA 2 against unsteady communication networks. We show that COHDA 2 is a very efficient decentralized heuristic that is able to tackle a distributed combinatorial optimization problem with regard to multiple local objective functions, as well as a common global objective function, without being dependent on centrally gathered knowledge.
In REF a fully decentralized agent-based approach for combinatorial optimization problems has been introduced.
Evaluation of a Self-organizing Heuristic for Interdependent Distributed Search Spaces.
ICAART
Computer Science