Distributed optimization with information-constrained population dynamics

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 s...

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Autores Principales: Pantoja, A., Obando, Germán, Quijano, N.
Formato: Artículo (Article)
Lenguaje:Inglés (English)
Publicado: Elsevier Ltd 2019
Materias:
Acceso en línea:https://repository.urosario.edu.co/handle/10336/22477
https://doi.org/10.1016/j.jfranklin.2018.10.016
id ir-10336-22477
recordtype dspace
spelling ir-10336-224772022-05-02T12:37:16Z Distributed optimization with information-constrained population dynamics Pantoja, A. Obando, Germán Quijano, N. Constrained optimization Electric load dispatching Graph theory Multi agent systems Population dynamics Scheduling Convergence rates Distributed optimization Economic dispatch problems Equilibrium point Global objective functions Local information Multiagent framework Topological constraints Problem solving 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. © 2018 The Franklin Institute 2019 2020-05-25T23:56:39Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 160032 https://repository.urosario.edu.co/handle/10336/22477 https://doi.org/10.1016/j.jfranklin.2018.10.016 eng info:eu-repo/semantics/openAccess application/pdf Elsevier Ltd instname:Universidad del Rosario
institution EdocUR - Universidad del Rosario
collection DSpace
language Inglés (English)
topic Constrained optimization
Electric load dispatching
Graph theory
Multi agent systems
Population dynamics
Scheduling
Convergence rates
Distributed optimization
Economic dispatch problems
Equilibrium point
Global objective functions
Local information
Multiagent framework
Topological constraints
Problem solving
spellingShingle Constrained optimization
Electric load dispatching
Graph theory
Multi agent systems
Population dynamics
Scheduling
Convergence rates
Distributed optimization
Economic dispatch problems
Equilibrium point
Global objective functions
Local information
Multiagent framework
Topological constraints
Problem solving
Pantoja, A.
Obando, Germán
Quijano, N.
Distributed optimization with information-constrained population dynamics
description 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. © 2018 The Franklin Institute
format Artículo (Article)
author Pantoja, A.
Obando, Germán
Quijano, N.
author_facet Pantoja, A.
Obando, Germán
Quijano, N.
author_sort Pantoja, A.
title Distributed optimization with information-constrained population dynamics
title_short Distributed optimization with information-constrained population dynamics
title_full Distributed optimization with information-constrained population dynamics
title_fullStr Distributed optimization with information-constrained population dynamics
title_full_unstemmed Distributed optimization with information-constrained population dynamics
title_sort distributed optimization with information-constrained population dynamics
publisher Elsevier Ltd
publishDate 2019
url https://repository.urosario.edu.co/handle/10336/22477
https://doi.org/10.1016/j.jfranklin.2018.10.016
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score 12,131701