Development of a method for increasing the efficiency of processing heterogeneous data using a metaheuristic algorithm - Ragulin, Vitaliy; Owaid, Salman Rasheed; Kuchuk, Heorhii; Andriienko, Serhii; Lytvynenko, Oleksandr; Ivanov, Evgen; Lyashenko, Anna; Momit, Alexander; Gaman, Oleksandr; Hurskyi, Taras (Eastern-European Journal of Enterprise Technologies, 2024)
The problems of processing heterogeneous data are discontinuous, undifferentiated, and multimodal. The most common approaches to processing heterogeneous data are swarm intelligence algorithms (swarm algorithms). Given the above, classical gradient deterministic algorithms are inappropriate for solving the problems of processing heterogeneous data. The problem solved in the study is to increase the efficiency of processing heterogeneous data circulating in information systems, regardless of the number of data sources. The object of the study is hierarchical systems. A method for increasing the efficiency of processing heterogeneous data using a metaheuristic algorithm is proposed. The study is based on the reptile algorithm (RA) for processing heterogeneous data circulating in the system. For RA training, evolving artificial neural networks are used. The originality of the proposed method lies in setting RA taking into account the uncertainty of the initial data, improved global and local search procedures. Also, the originality of the study lies in determining RA feeding locations, which allows prioritizing the search in a given direction. The next element in the originality of the study is the possibility of choosing an RA hunting strategy, which allows a rational use of available system computing resources. Another original element of the study is determining the initial velocity of each RA. This makes it possible to optimize the speed of exploration of each RA in a certain direction. The method provides a 15–19 % increase in data processing efficiency by using additional improved procedures. The proposed method should be used in processing large amounts of data