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• W2899286960 abstract "In a multi-agent framework, distributed optimization problems are generally described as the minimization of a global objective function, where each agent can get information only from a neighborhood defined by a network topology. To solve the problem, this work presents an information-constrained strategy based on population dynamics, where payoff functions and tasks are assigned to each node in a connected graph. We prove that the so-called distributed replicator equation (DRE) converges to an optimal global outcome by means of the local-information exchange subject to the topological constraints of the graph. To show the application of the proposed strategy, we implement the DRE to solve an economic dispatch problem with distributed generation. We also present some simulation results to illustrate the theoretic optimality and stability of the equilibrium points and the effects of typical network topologies on the convergence rate of the algorithm." @default.
• W2899286960 created "2018-11-09" @default.
• W2899286960 creator A5010955102 @default.
• W2899286960 creator A5036133172 @default.
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• W2899286960 date "2019-01-01" @default.
• W2899286960 modified "2023-10-14" @default.
• W2899286960 title "Distributed optimization with information-constrained population dynamics" @default.
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• W2899286960 doi "https://doi.org/10.1016/j.jfranklin.2018.10.016" @default.
• W2899286960 hasPublicationYear "2019" @default.
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