Distributed neurodynamic optimization for optimal multicluster resource allocation with cardinality constraints
Meng Hsien Lin, Zicong Xia, Shuting Sun, Yang Liu · Information Sciences · 2025
In this paper, a distributed neurodynamic optimization method is developed for a class of multicluster resource allocation models with 0-1 integer constraints and cardinality constraints. In the optimization model, the objective function is the sum of multiple clusters of convex local objective functions with 0-1 integer constraints that lead to nonconvexity; additionally, the resource allocation model is subject to globally coupled resource allocation constraints and bound constraints, and the cardinality constraints are introduced to limit the total number of resource-allocated points in each cluster. To address challenges caused by the nonconvexity and hybrid constraints, a distributed neurodynamic optimization method based on an augmented Lagrangian function is developed, and it is proven to converge to a local minimum. The validity of the main results is demonstrated via two examples involving a power system.