Investigating and Comparing the Performance of meta-heuristic Algorithms in Optimum Knowledge Extraction from Business Data (Genetic Algorithm, Particle Swarm and Whale)

With the advancement of science and technology, organizations are daily faced with increasing volumes of stored data and reducing their needed extraction knowledge. Regarding this issue, it is necessary to use the best techniques to extract knowledge from organizational data in order to prevent the storage of surplus data in the organizational database. One of the proposed optimization algorithms to find optimal point is the Whale optimization algorithm, which performs this by imitation of biological or physical phenomena. This algorithm searches the stored records to extract the best possible knowledge from the considered record data. Since optimal design in the search space is not identifiable as a prioritization, actually identified the best location for the extraction of knowledge assuming that this location is the best location or at least closest to the optimal state, and in fact, by displaying the extracted knowledge to the managers in making strategic decisions at the right time. In this paper, an intelligent model with Whale algorithm is presented to identify the optimal knowledge points. The structural and technical capability of this model is compared with other models, such as genetics and particles swarm