A collaborative neurodynamic approach to global and combinatorial optimization. (June 2019)

• Record Type: Journal Article
• Authors: Che, Hangjun; Wang, Jun
• Is Part Of: Neural networks. Volume 114(2019)
• Page Start: 15
• Page End: 27
• Publication Date: 2019-06
• Subjects: Global optimization -- Combinatorial optimization -- Augmented Lagrangian function -- Collaborative neurodynamic approach; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2019.02.002
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: In this paper, a collaborative neurodynamic optimization approach is proposed for global and combinatorial optimization. First, a combinatorial optimization problem is reformulated as a global optimization problem. Second, a neurodynamic optimization model based on an augmented Lagrangian function is proposed and its states are proven to be asymptotically stable at a strict local minimum in the presence of nonconvexity in objective function or constraints. In addition, multiple neurodynamic optimization models are employed to search for global optimal solutions collaboratively and particle swarm optimization (PSO) is used to optimize their initial states. The proposed approach is shown to be globally convergent to global optimal solutions as substantiated for solving benchmark problems.