Distributed Control of Autonomous Swarms by Using Parallel Simulated Annealing Algorithm

In early work of the authors, it was shown that Gibbs sampler based sequential annealing algorithm could be used to achieve self-organization in swarm vehicles based only on local information. However, long travelling time presents barriers to implement the algorithm in practice. In this paper we study a popular acceleration approach, the parallel annealing algorithm, and its convergence properties. We first study the convergence and equilibrium properties of the synchronous parallel sampling algorithm. A special example based on a battle field scenario is then studied. Sufficient conditions that the synchronous algorithm leads to desired configurations (global minimizers) are derived. While the synchronized algorithm reduces travelling time, it also raises delay and communication cost dramatically, in order to synchronize moves of a large group of vehicles. An asynchronous version of the parallel sampling algorithm is then proposed to solve the problem. Convergence properties of the asynchronous algorithm are also investigated