Distributed optimization with information-constrained population dynamics

Andres Pantoja, German Dario Obando Bravo, Nicanor Quijano

    Research output: Contribution to journalResearch Articlepeer-review

    18 Scopus citations


    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.
    Translated title of the contributionOptimización distribuida usando dinámicas poblacionales con información restringida
    Original languageEnglish (US)
    Pages (from-to)209-236
    Number of pages28
    JournalJournal of the Franklin Institute
    Issue number1
    StatePublished - Jan 1 2019

    All Science Journal Classification (ASJC) codes

    • Control and Systems Engineering
    • Signal Processing
    • Computer Networks and Communications
    • Applied Mathematics


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