Huber Loss Function Based on Cockroach Swarm Algorithm with T-Distribution Parameters

In many electronic and information engineering related disciplines, various complex machine learning and reinforcement learning optimization algorithms appear nowadays so as to handle huge amount of data related. Traditional optimization methods, which have to take the whole data space into consideration, cannot always perform well, and cannot always be completed in a short period of time as the data amount to be handled is huge. The object of the so-called optimization problem is to find the optimal solutions or quasi-optimal solutions in a complex and huge data searching space, and finding the efficient optimization algorithm which can handle data with characteristics of complexity, nonlinearity, and modeling difficulties for practical engineering, can be meaningful for electronic and information industries. Huber loss function is a typical traditional optimization method, with the error expressed via smooth mean absolute error (MAE) which has the advantages of MAE and mean square error (MSE) and uses MAE to enhance the robustness of MSE for those abnormal data points. However, Huber loss function employs gradient descent (GD) method to find the minimum value, which can easily be trapped into the local optimal solution. In this paper, a hybrid optimization algorithm, Huber loss function based on cockroach swarm algorithm with t-distribution has been proposed, it has better global convergence, and via numerical experiment, its efficiency in calculation has also been proved.