Distributed Multiobjective Resource Allocation for Heterogeneous Systems over Directed Graph

This paper investigates a distributed multiobjective resource allocation problem for heterogeneous multiagent systems under directed graph. This framework contains a series of distributed decision makers, where each decision maker has a set of conflicting objective functions. We introduce a distributed algorithm so as to obtain weighting factors and the desirable points of every objective function, which are used to gain a local preference with the help of the weighted L_h preference index within finite time. It turns out that there is a unique Pareto solution for this problem. Next, a out-degree based design law for heterogeneous systems is put forward for minimizing entire local objective functions constrained by limited resource. Finally, two numerical instances are included to validate the advantage of distributed optimization algorithms.