This paper presents a comparative analysis of bioinspired algorithms employed on a PV system subject to standard conditions, under step-change of irradiance conditions, and a partial shading condition for tracking the global maximum power point (GMPP). Four performance analysis and comparison techniques are artificial bee colony, particle swarm optimization, genetic algorithm, and a new metaheuristic technique called jellyfish optimization, respectively. These existing algorithms are well-known for tracking the GMPP with high efficiency. This paper compares these algorithms based on extracting GMPP in terms of maximum power from a PV module running at a uniform (STC), nonuniform solar irradiation (under step-change of irradiance), and partial shading conditions (PSCs). For analysis and comparison, two modules are taken: 1Soltech-1STH-215P and SolarWorld Industries GmbH Sunmodule plus SW 245 poly module, which are considered to form a panel by connecting four series modules. Comparison is based on maximum power tracking, total execution time, and minimum number of iterations to achieve the GMPP with high tracking efficiency and minimum error. Minitab software finds the regression equation (objective function) for STC, step-changing irradiation, and PSC. The reliability of the data (P-V curves) was measured in terms of p value, R, R2 , and VIF. The R2 value comes out to be near 1, which shows the accuracy of the data. The simulation results prove that the new evolutionary jellyfish optimization technique gives better results in terms of higher tracking efficiency with very less time to obtain GMPP in all environmental conditions, with a higher efficiency of 98 to 99.9% with less time of 0.0386 to 0.1219 sec in comparison to ABC, GA, and PSO. The RMSE value for the proposed method JFO (0.59) is much lower than that of ABC, GA, and PSO.

A distance metric known as non‐Euclidean distance deviates from the laws of Euclidean geometry, which is the geometry that governs most physical spaces. It is utilized when Euclidean distance is inappropriate, for as when dealing with curved surfaces or spaces with complex topologies. The ability to apply deep learning techniques to non‐Euclidean domains including graphs, manifolds, and point clouds is made possible by non‐Euclidean deep learning. The use of non‐Euclidean deep learning is rapidly expanding to study real‐world datasets that are intrinsically non‐Euclidean. Over the years, numerous novel techniques have been introduced, each with its benefits and drawbacks. This paper provides a categorized archive of non‐Euclidean approaches used in computer vision up to this point. It starts by outlining the context, pertinent information, and the development of the field’s history. Modern state‐of‐the‐art methods have been described briefly and categorized by application fields. It also highlights the model’s shortcomings in tables and graphs and shows different real‐world applicability. Overall, this work contributes to a collective information and performance comparison that will help enhance non‐Euclidean deep‐learning research and development in the future.