Constrained distributed optimization based on population dynamics

This paper proposes a novel methodology for solving constrained optimization problems in a distributed way, inspired by population dynamics and adding dynamics to the population masses. The proposed methodology divides the problem into smaller problems, whose feasible regions vary over time achieving an agreement to solve the global problem. The methodology also guarantees attraction to the feasible region and allows to have few changes in the decision-making design, when the network suffers the addition or removal of nodes. Simulation results are presented in order to illustrate several cases.