Improved Neural Network Optimization Algorithm Based on Chaotic Adaptive Search

The original neural network optimization algorithm (NNA) faces issues like weak global search capability, susceptibility to local optima in the later optimization stages, and inadequate convergence accuracy. To address these, we propose a chaotic adaptive search based on an improved neural network optimization algorithm (CASNNA). Firstly, we introduce a logistic chaotic mapping to mitigate uneven population distribution within the solution space, thereby expanding the search space and enhancing the likelihood of finding the global optimal solution. Secondly, a spiral search position update strategy is incorporated to balance global and local searches by dynamically adjusting the spiral shape, thus enhancing the algorithm's search efficiency and accuracy. Finally, an adaptive t-distribution strategy is employed, with a t-distribution mutation disturbance factor added during the later stages of algorithm iteration to escape local optima, thus improving the convergence speed. Simulation experiments were conducted using internationally recognized benchmark test functions to compare CASNNA with other meta-heuristic algorithms. The results demonstrate that the proposed CASNNA algorithm excels in terms of convergence speed and accuracy.