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The GEO (Golden Eagle Optimizer) is a population based meta heuristic algorithm that simulates the cooperative hunting behavior of golden eagles. In view of the problem of poor solution accuracy and local optima traps in the GEO algorithm, this study proposes an improved MERGEO (Mapped Elitist Reverse GEO) algorithm. Based on the original algorithm, symmetric mapping search strategy, adaptive elite strategy and random backward learning mechanism, are used to balance the exploration and development stages of the algorithm, and obtain the ability to avoid local optimal and better optimization accuracy. The independent strategy effectiveness analysis, scalability analysis and optimization performance comparison with other algorithms are carried out on 10 benchmark test functions. The experimental results show that the improved MERGEO algorithm has strong competitiveness and good optimization ability. The improved algorithm is applied to the coverage optimization problem of wireless sensor networks and pressure vessel design problem, which verifies the practical application value of improved algorithm. [ABSTRACT FROM AUTHOR]

Big Data analytics performance is critical in the dynamic world of Industry 5.0, where human engagement with cutting-edge technology is essential. Based on a comparison experiment, this empirical research highlights the significance of optimal data processing algorithms by providing important insights into the relationship between data amount and processing speed. The requirement of resource-intensive demands for efficient resource allocation and optimization in Industry 5.0 operations is emphasized. Operation C's exceptional performance in terms of mistake rates, data correctness, and processing quality highlights the need of careful data management procedures. As Industry 5.0 develops, scalability becomes more important. Operation C is a perfect example of how to adapt to higher data volumes. The way forward for an industrial future that is more responsive, sustainable, and efficient is shaped by this study.

In this article we construct parallel solvers analyze the efficiency and accuracy of general parallel solvers for three dimensional parabolic problems with the fractional power of elliptic operators. The proposed discrete method are targeted for general non-constant elliptic operators, the second motivation for the usage of such schemes arises when non-uniform space meshes are essential. Parallel solvers are required to solve the obtained large size systems of linear equations. The detailed scalability analysis is done in order to compare the efficiency of prposed parallel algorithms. Results of computational experiments are presented and analyzed.