A Collective Neurodynamic Approach to Constrained Global Optimization

Zheng Yan, Jianchao Fan, Jun Wang

Research output: Contribution to journalArticlepeer-review

66 Citations (Scopus)


Global optimization is a long-lasting research topic in the field of optimization, posting many challenging theoretic and computational issues. This paper presents a novel collective neurodynamic method for solving constrained global optimization problems. At first, a one-layer recurrent neural network (RNN) is presented for searching the Karush-Kuhn-Tucker points of the optimization problem under study. Next, a collective neuroydnamic optimization approach is developed by emulating the paradigm of brainstorming. Multiple RNNs are exploited cooperatively to search for the global optimal solutions in a framework of particle swarm optimization. Each RNN carries out a precise local search and converges to a candidate solution according to its own neurodynamics. The neuronal state of each neural network is repetitively reset by exchanging historical information of each individual network and the entire group. Wavelet mutation is performed to avoid prematurity, add diversity, and promote global convergence. It is proved in the framework of stochastic optimization that the proposed collective neurodynamic approach is capable of computing the global optimal solutions with probability one provided that a sufficiently large number of neural networks are utilized. The essence of the collective neurodynamic optimization approach lies in its potential to solve constrained global optimization problems in real time. The effectiveness and characteristics of the proposed approach are illustrated by using benchmark optimization problems.

Original languageEnglish
Article number7445870
Pages (from-to)1206-1215
Number of pages10
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number5
Publication statusPublished - May 2017
Externally publishedYes


  • Collective neurodynamics
  • Global optimization
  • Recurrent neural networks (RNNs)


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