TY - JOUR
T1 - Distributed nonconvex optimization of multiagent systems using boosting functions to escape local optima
AU - Welikala, Shirantha
AU - Cassandras, Christos G.
N1 - Publisher Copyright:
© 1963-2012 IEEE.
PY - 2021/11/1
Y1 - 2021/11/1
N2 - 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.
AB - 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.
KW - Boosting functions
KW - distributed optimization
KW - multiagent systems
KW - nonconvex optimization
UR - http://www.scopus.com/inward/record.url?scp=85119419470&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85119419470&partnerID=8YFLogxK
U2 - 10.1109/TAC.2020.3034869
DO - 10.1109/TAC.2020.3034869
M3 - Article
AN - SCOPUS:85119419470
SN - 0018-9286
VL - 66
SP - 5175
EP - 5190
JO - IEEE Transactions on Automatic Control
JF - IEEE Transactions on Automatic Control
IS - 11
ER -