Coevolutionary Neural Dynamics Considering Multiple Strategies for Nonconvex Optimization

In the field of practical applications, the solution of nonconvex optimization problems plays a crucial role. However, many practical applications often encounter perturbations that may affect solutions to relevant nonconvex problems. Such perturbations are typically unavoidable. Moreover, in the presence of perturbations, most algorithms for nonconvex optimization suffer from low solution accuracy and a tendency to become trapped in local optima. To address this limitation, this paper proposes a coevolutionary neural dynamics considering multiple strategies (CNDMS) model. Firstly, a modified neural dynamics model with a dual-gradient accumulation term is constructed as a local search operator, which…

• LU
  Lanzhou University
• NN
  National Natural Science Foundation of China

  Award: 62476115
• FR
  Fundamental Research Funds for the Central Universities