Logistic Regression Based on Faure Sequence Initialized Fruit Fly Optimization Algorithm

As a regression algorithm, logistic regression can be used for not only regression prediction, but also classification. By employing Sigmod function of nonlinear transformation to obtain a value within (0, 1), it can be given the characteristic of probability in logistic regression. According to the rule “value is 1 when
$p > 0.5$
, and value is 0 when
$p < 0.5$
”, logistic regression can be recognized as a 2-class classification problem and is highly explanatory. However, in the process of solving the parameters of the loss function of traditional logistic regression, gradient descent (GD) method is often used, which means that there is a requirement for the convexity of the function, and it is necessary to reasonably control the size of the hyperparameter in GD, otherwise it is easy to fall into the local optimum value and cannot obtain the global optimal solution. As new methods need to be found to solve the problem of loss function optimization, in this paper, a Faure sequence initialized fruit fly optimization algorithm (FSFFOA) method has been proposed to replace GD method to complete the optimization process. As a swarm intelligence algorithm, FSFFOA has many parameters that can be used to adjust the algorithm accuracy and speed, and has good performance in solving nonlinear optimization problems. Via numerical experiment, the effectiveness of FSFFA has been proved.