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[2F4-GS-5-01] A Study of Decentralized Optimization Method Based on Evolutionary Algorithm Under Message Loss
Keywords:Distributed Constraint Optimization, Evolutionary Algorithm, Message Loss
Decentralized optimization problems and solutions methods on multi-agent systems, including distributed constraint optimization problems, have been studied as the basis for distributed resource allocation and cooperative decision making.
In the implementation of practical applications, a situation in which the communication among several agents is temporarily missing during the decision-making process might be allowed to assure some real-time performance.
As an investigation of the resilience of the solution methods to such situations, we experimentally analyze the influence of message loss in the solution method based on evolutionary algorithm for distributed constraint optimization problems.
We also present mitigation approaches that considers the missing agents.
In the implementation of practical applications, a situation in which the communication among several agents is temporarily missing during the decision-making process might be allowed to assure some real-time performance.
As an investigation of the resilience of the solution methods to such situations, we experimentally analyze the influence of message loss in the solution method based on evolutionary algorithm for distributed constraint optimization problems.
We also present mitigation approaches that considers the missing agents.
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