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The processes of retrieving useful information from a dataset are an important data mining technique that is commonly applied, known as Data Clustering. Recently, nature-inspired algorithms have been proposed and utilized for solving the optimization problems in general, and data clustering problem in particular. Black Hole (BH) optimization algorithm has been underlined as a solution for data clustering problems, in which it is a population-based metaheuristic that emulates the phenomenon of the black holes in the universe. In this instance, every solution in motion within the search space represents an individual star. The original BH has shown a superior performance when applied on a benchmark dataset, but it lacks exploration capabilities in some datasets. Addressing the exploration issue, this paper introduces the levy flight into BH algorithm to result in a novel data clustering method ''Levy Flight Black Hole (LBH)'', which was then presented accordingly. In LBH, the movement of each star depends mainly on the step size generated by the Levy distribution. Therefore, the star explores an area far from the current black hole when the value step size is big, and vice versa. The performance of LBH in terms of finding the best solutions, prevent getting stuck in local optimum, and the convergence rate has been evaluated based on several unimodal and multimodal numerical optimization problems. Additionally, LBH is then tested using six real datasets available from UCI machine learning laboratory. The experimental outcomes obtained indicated the designed algorithm's suitability for data clustering, displaying effectiveness and robustness.

The application of meta-heuristic algorithms for t-way testing has recently become prevalent. Consequently, many useful meta-heuristic algorithms have been developed on the basis of the implementation of t-way strategies (where t indicates the interaction strength). Mixed results have been reported in the literature to highlight the fact that no single strategy appears to be superior compared with other configurations. The hybridization of two or more algorithms can enhance the overall search capabilities, that is, by compensating the limitation of one algorithm with the strength of others. Thus, hybrid variants of the flower pollination algorithm (FPA) are proposed in the current work. Four hybrid variants of FPA are considered by combining FPA with other algorithmic components. The experimental results demonstrate that FPA hybrids overcome the problems of slow convergence in the original FPA and offers statistically superior performance compared with existing t-way strategies in terms of test suite size.

Due to their enhanced performance and simplicity in manufacturing, scalability, and versatility, lead-halide perovskite-based solar cells (HPSCs) have received much attention in the domains of energy. Lead is present in nature as a poisonous substance that causes various issues to climate and human health and prevents its further industrialization. Over the past few years, there has been a noticeable interest in exploring some alternative lead-free perovskites. However, owing to some intrinsic losses, the performance that may be achieved from these photovoltaics is not up to standards. Thus, for the purpose of efficiency improvement, a comprehensive simulation is required to comprehend the cause of these losses. In the current research, an investigation into how to employ the promisingly efficient lead-free, allinorganic cesium tin−germanium iodide (CsSnGeI 3 ) perovskites as the photoactive layer in HPSCs was performed. Results exhibited a high efficiency of 12.95% with a CsSn 0.5 Ge 0.5 I 3 perovskite thickness of 0.6 μm and a band gap of 1.5 eV at room temperature. High efficiency may be achieved using phenyl-C61-butyric acid methyl ester (PCBM) as an electron transport material because of its favorable energy-level alignment with the perovskite material. The research further tested the perovskite layer thickness and defect density in depth. The results showed that the carrier diffusion lengths have a big effect on how well the HPSC works.