Adaptive Niching-Based Gradient-Accelerated Differential Evolution for High-Dimensional Nonconvex Optimization

Summary

Researchers propose AdaptiveGDE, a differential evolution algorithm for high-dimensional nonconvex optimization. It uses a two-step mutation operator that separates differential mutation from gradient descent, and an adaptive niching strategy that adjusts subpopulation count based on population similarity and iteration progress. Under relaxed smoothness assumptions and approximate ℓ1 regularization, the authors prove convergence in expectation to a near-optimal solution within O(1/ε^4) iterations, and report improved test accuracy and loss in DNN training, especially with limited data.