Abstract
Distributed optimization methods have been studied recently motivated by emerging applications in smart grid, multi-robot, etc. In most of the studies, convexity and smoothness of the objective and constraint functions are assumed while such assumptions are not always made in practice. This paper presents a distributed particle swarm optimization algorithm based on primal-dual decomposition architectures so that any gradient information is not needed. To investigate the potential of the presented algorithm, some simple numerical examples are provided while comparing with the conventional distributed primal-dual perturbation algorithm.
