Fractional weightage based objective function to a hybrid optimization algorithm for model transformation

Abstract

Model transformation (MT) contributes a major role in the model-driven engineering (MDE), which are used to transfer the models among various languages to refactor and simulate the models or to obtain the useful codes from the models. Thus, the need for MT in MDE is very important, but the practical methods are not suitable for the detection of errors in transformations. This paper proposes an advanced algorithm, called fractional whale optimization integrated adaptive dragonfly (F-WOADF) algorithm to perform the MT from the class diagram (CLD) to relational schema (RS) model. The proposed algorithm modifies the adaptive dragonfly (ADF) algorithm with the concept of whale optimization algorithm (WOA) using the fractional theory. The UML CLD is transformed into the RS model using the optimal blocks that are selected with the use of the proposed algorithm. The performance of the F-WOADF method is evaluated using automatic correctness (AC), and fitness measure. The proposed method produces the maximum AC of 0.8583 and the maximum fitness measure of 0.8984 that indicates the effectiveness of the proposed method.