Differential Search Algorithm by XOR Gate in Binary Optimization Problems

There are many meta-heuristic algorithms proposed by researchers, and the general feature of these algorithms is that they are modeled by being inspired by the intelligent behaviors of living things in nature, individually or as a group. Basic versions of meta-heuristic algorithms are frequently introduced to solve continuous optimization problems. However, due to their flexibility, they can be adjusted for use in various types of optimization problems. In this research, an improved version of the Differential Search Algorithm (DSAX), which is a population-based meta-heuristic algorithm that has shown its success in solving optimization problems, is proposed to use in solving binary optimization problems. The DSAX has been tested on large-scale binary optimization problems. Moreover, it is compared with the XOR-Based version of the Tree-Seed Algorithm, which is well known in the literature. According to the obtained results and the comparisons, it is seen that the DSAX algorithm proposed in this study is superior.