  We address the problem of multiple local optima arising due to non-convex
objective functions in cooperative multi-agent 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 non-zero magnitude.
We develop a Distributed Boosting Scheme (DBS) 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 multi-agent
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.
