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Randomized optimal consensus of multiagent systems based on a novel intermittent projected subgradient algorithm
Optimal Control Applications and Methods ( IF 1.8 ) Pub Date : 2020-07-07 , DOI: 10.1002/oca.2643 Zhengqing Shi 1 , Chuan Zhou 1
Optimal Control Applications and Methods ( IF 1.8 ) Pub Date : 2020-07-07 , DOI: 10.1002/oca.2643 Zhengqing Shi 1 , Chuan Zhou 1
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In this article, a novel intermittent projected subgradient algorithm is presented to solve the randomized optimal consensus problem for heterogeneous multiagent systems with time‐varying communication topologies. The multiagent systems achieve the consensus meanwhile minimizing the global objective function via the proposed algorithm, where fi(x) is the convex objective function of agent i itself. Due to the common Bernoulli distribution adopted in the existing random optimization algorithm without considering the different computing capability of each agent. An individual projection probability is assigned for each agent based on computing capabilities so that either making projection or taking average is chosen according to the above probability which can effectively avoid overload for some agents with lower computing capabilities and improve the reliability of the overall systems. A new sufficient step‐size condition is given to ensure all agents converge to the optimal solution with probability one. Finally, a numerical example is also given to validate the proposed method.
中文翻译:
基于新型间歇投影次梯度算法的多主体系统随机最优共识
在本文中,提出了一种新颖的间歇投影次梯度算法,以解决具有时变通信拓扑的异构多主体系统的随机最优共识问题。所述多代理系统实现的共识同时最小化全局目标函数通过所提出的算法,其中˚F我(X)是剂凸状目标函数我本身。由于现有随机优化算法采用了通用的伯努利分布,因此没有考虑每个代理的不同计算能力。根据计算能力为每个代理分配一个单独的预测概率,以便根据上述概率选择进行投影或取平均值,这可以有效地避免某些计算能力较低的代理过载,并提高整个系统的可靠性。给出了一个新的足够的步长条件,以确保所有代理以概率1收敛到最优解。最后,通过数值算例验证了该方法的有效性。
更新日期:2020-07-07
中文翻译:
基于新型间歇投影次梯度算法的多主体系统随机最优共识
在本文中,提出了一种新颖的间歇投影次梯度算法,以解决具有时变通信拓扑的异构多主体系统的随机最优共识问题。所述多代理系统实现的共识同时最小化全局目标函数通过所提出的算法,其中˚F我(X)是剂凸状目标函数我本身。由于现有随机优化算法采用了通用的伯努利分布,因此没有考虑每个代理的不同计算能力。根据计算能力为每个代理分配一个单独的预测概率,以便根据上述概率选择进行投影或取平均值,这可以有效地避免某些计算能力较低的代理过载,并提高整个系统的可靠性。给出了一个新的足够的步长条件,以确保所有代理以概率1收敛到最优解。最后,通过数值算例验证了该方法的有效性。