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We address the problem of multiple local optima arising due to non-convexobjective functions in cooperative multi-agent optimization problems. To escapesuch local optima, we propose a systematic approach based on the concept ofboosting functions. The underlying idea is to temporarily transform thegradient at a local optimum into a boosted gradient with a non-zero magnitude.We develop a Distributed Boosting Scheme (DBS) based on a gradient-basedoptimization algorithm using a novel optimal variable step size mechanism so asto guarantee convergence. Even though our motivation is based on the coveragecontrol problem setting, our analysis applies to a broad class of multi-agentproblems. Simulation results are provided to compare the performance ofdifferent boosting functions families and to demonstrate the effectiveness ofthe boosting function approach in attaining improved (still generally local)optima.

Distributed Non-convex Optimization of Multi-agent Systems Using Boosting Functions to Escape Local Optima: Theory and Applications