The equalization of digital channels is generally known to be a nonlinear classification problem. Applications such as these can benefit from networks that approximate nonlinear mappings. It gets good performance by adjusting only one coefficient and one center closest to the input vector of the radial basis function network (RBFN), which is a simplified version of stochastic gradient techniques. Artificial Neural Networks (ANNs) are suitable for channel equalization because they have the capability to map between variables. Having only one hidden layer in Radial Basis Function Neural Network (RBFNN) makes it the most preferred equalizer to mitigate distortions in the channels. The ability to equalize nonlinear channels is strength of radial RBFNN, which is a simplified version of stochastic gradient techniques. The conventional “hit and trial” approach poses the greatest difficulty when designing RBFNN Equalizers. As a solution to these limitations, this work suggests a training strategy based on Gaussian distribution estimation strategy (GDE) in hybrid butterfly optimization algorithm (GDEBOA) for RBFNN channel equalizer. The proposed RBFNN equalizer is being trained using a population‐based optimization algorithm. An algorithm, called GDEBOA, based on a GDE approach is developed in this paper. The proposed training scheme significantly outperforms existing metaheuristic algorithms in terms of mean square error (MSE) and bit error rate (BER). Furthermore, based on burst error scenarios and bit error probability (BEP), the proposed method has demonstrated greater robustness than other methods when faced with such scenarios. The effectiveness of the suggested scheme over a wide range of signal‐to‐noise ratios has been validated by several simulation studies. Additionally, statistical importance of the suggested technique is analyzed. It is visualized that GDEBOA performs better than existing algorithms for training RBFNN equalizer.
