Distributed nonconvex optimization of multiagent systems using boosting functions to escape local optima

Shirantha Welikala, Christos G. Cassandras

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

In this article, we address the problem of multiple local optima arising due to nonconvex objective functions in cooperative multiagent optimization problems. To escape such local optima, we propose a systematic approach based on the concept of boosting functions. The underlying idea is to temporarily transform the gradient at a local optimum into a boosted gradient with a nonzero magnitude. We develop a distributed boosting scheme based on a gradient-based optimization algorithm using a novel optimal variable step size mechanism so as to guarantee convergence. Even though our motivation is based on the coverage control problem setting, our analysis applies to a broad class of multiagent problems. Simulation results are provided to compare the performance of different boosting functions families and to demonstrate the effectiveness of the boosting function approach in attaining improved (still generally local) optima.

Original languageEnglish
Pages (from-to)5175-5190
Number of pages16
JournalIEEE Transactions on Automatic Control
Volume66
Issue number11
DOIs
StatePublished - 1 Nov 2021

Keywords

  • Boosting functions
  • distributed optimization
  • multiagent systems
  • nonconvex optimization

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