Coevolutionary Neural Dynamics With Learnable Parameters for Nonconvex Optimisation

Nonconvex optimisation plays a crucial role in science and industry. However, existing methods often encounter local optima or provide inferior solutions when solving nonconvex optimisation problems, lacking robustness in noise scenarios. To address these limitations, we aim to develop a robust, efficient and globally convergent solver for nonconvex optimisation. This is achieved by combining the efficient local exploitation ability of a parameter‐learnt neural dynamics (PLND) model with the global search capability of the coevolutionary mechanism. We combine their characteristics to design a coevolutionary neural dynamics with learnable parameters (CNDLP) model. The gradient information is used to find the optimal solution more effectively, and neural dynamics models have robustness, which ensures that the influence of noise can be effectively suppressed in the calculation process. Theoretical analyses show the global convergence and robustness of the designed CNDLP model. Numerical experiments on 9 benchmark functions and a practical engineering design example are conducted with five existing meta‐heuristic algorithms. Benchmarks cover diverse problems, from classical landscapes like benchmark Shubert to high‐dimensional cases such as 30‐dimensional Rosenbrock. Results confirm CNDLP's excellent performance in both solution quality and convergence speed under noise.