Distributed Nonconvex Optimization of Multiagent Systems Using Boosting Functions to Escape Local Optima

• 成果类型: Article
• 署名作者: Welikala, Shirantha; Cassandras, Christos G.
• 署名单位: Boston University; Boston University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3034869
• 发表日期: 2021
• 页码: 5175-5190
• 关键词: Boosting; linear programming; optimization; Multi-agent systems; Satellite broadcasting; Space exploration; systematics; Boosting functions; distributed optimization; multiagent systems; Nonconvex Optimization

摘要

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.