### Resumen

Idioma original | English (US) |
---|---|

Páginas (desde-hasta) | 209-236 |

Número de páginas | 28 |

Publicación | Journal of the Franklin Institute |

Volumen | 356 |

N.º | 1 |

DOI | |

Estado | Published - ene 1 2019 |

### All Science Journal Classification (ASJC) codes

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

### Citar esto

*Journal of the Franklin Institute*,

*356*(1), 209-236. https://doi.org/10.1016/j.jfranklin.2018.10.016

}

*Journal of the Franklin Institute*, vol. 356, n.º 1, pp. 209-236. https://doi.org/10.1016/j.jfranklin.2018.10.016

**Distributed optimization with information-constrained population dynamics.** / Pantoja, Andres; Obando Bravo, German Dario; Quijano, Nicanor.

Resultado de la investigación: Contribución a Revista › Artículo

TY - JOUR

T1 - Distributed optimization with information-constrained population dynamics

AU - Pantoja, Andres

AU - Obando Bravo, German Dario

AU - Quijano, Nicanor

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85056770633&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85056770633&partnerID=8YFLogxK

U2 - 10.1016/j.jfranklin.2018.10.016

DO - 10.1016/j.jfranklin.2018.10.016

M3 - Article

AN - SCOPUS:85056770633

VL - 356

SP - 209

EP - 236

JO - Journal of the Franklin Institute

JF - Journal of the Franklin Institute

SN - 0016-0032

IS - 1

ER -