A collaborative neurodynamic approach to global and combinatorial optimization

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

34 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)15-27
Journal / PublicationNeural Networks
Volume114
Online published21 Feb 2019
Publication statusPublished - Jun 2019

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.

Research Area(s)

  • Augmented Lagrangian function, Collaborative neurodynamic approach, Combinatorial optimization, Global optimization