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Particle swarm optimization algorithms (PSOA) is a metaheuristic algorithm used to optimize computational problems using candidate solutions or particles based on selected quality measures. Despite the extensive research published, studies that critically examine its recent scientific developments and research impact are lacking. Therefore, the publication trends and research landscape on PSOA research were examined. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and bibliometric analysis techniques were applied to identify and analyze the published documents indexed in Scopus from 2001 to 2021. The published documents on PSOA increased from 8 to 1,717 (21,362.50%) due to the growing applications of PSOA in solving computational problems. "Conference papers" is the most common document type, whereas the most prolific researcher on PSOA is Andries P. Engelbrecht (South Africa). The most active affiliation (Ministry of Education) and funding organization (National Natural Science Foundation) are based in China. The research landscape on PSOA revealed high levels of publications, citations, and collaborations among the top authors, institutions, and countries worldwide. Keywords co-occurrence analysis revealed that "particle swarm optimization (PSO)" occurred more frequently than others. The findings of the study could provide researchers and policymakers with insights into the prospects and challenges of PSOA research relative to similar algorithms in the literature. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms are increasingly being utilized as a global optimal method in the inversion and modeling of magnetic data. We proposed the Bat Algorithm Optimization (BAO) technique that is based on bat echolocation performance to find the global optimum solution. The best-estimated source parameters that correspond to the objective function minimum value are obtained after achieving the global optimum (best) solution. The suggested BAO technique does not require any prior knowledge; rather, it is a global search method that provides an effective tool for scanning the space of data to appraise sources parameters. The BAO technique is applied to magnetic data in the class of dipping and vertical dikes along 2D profiles to estimate the dimensional source parameters that include the depth to top, origin location, amplitude coefficient, index angle of magnetization, and width of the dipping dikes. The BAO technique has been used for single and multiple dikes structures. The accuracy and stability of the BAO technique are achieved on different synthetic examples of free and noisy data for single and multiple cases. Furthermore, the presented BAO technique was effectively utilized in three field examples from China and Egypt for iron ore deposits and metavolcanics basalt rock investigations. Overall, the BAO technique recovered inversion outcomes are in good agreement with borehole, geology, and published literature results. [ABSTRACT FROM AUTHOR]

[Display omitted] • Three hybrid RF models were developed to predict blast-induced overbreak. • The RF-TSA model outperformed other predictive RF models in estimating overbreak. • The blast design parameters showed a deep impact on the overbreak generation. • The PF was selected as the most influential factor in the overbreak generation. Overbreak is a detrimental phenomenon caused by tunnel blasting, which can lead to increased time and cost in the construction schedule. It is very important to establish a model that can accurately predict the overbreak caused by tunnel blasting. To achieve this goal, the random forest (RF) is an ensemble machine learning model optimised by three metaheuristic algorithms to predicted overbreak, i.e. the grey wolf optimiser (GWO), the whale optimisation algorithm (WOA), and the tunicate swarm algorithm (TSA). The primary roles of GWO, WOA, and TSA are to search for the optimal hyper-parameters of the RF model in the solution space. To create the models above, 523 data samples were taken from a highway tunnel in China. The established database comprised seven predictors or inputs, including the number of holes, hole depth, total charge, advance length, rock mass rating, tunnel cross-sectional area, and powder factor. Three hybrid RF-based models (RF-GWO, RF-WOA, and RF-TSA) were constructed to predict overbreak. Subsequently, the performance levels of the developed hybrid models were evaluated according to four indices: the coefficient of determination, the root mean square error, the variance accounted for, and the A-20 index. The results showed that the TSA optimisation algorithm was better than the other two algorithms (WOA and GWO) at finding the best hyper-parameters for the RF model. Moreover, the results of comparative analysis with the single RF model confirmed that the proposed RF-TSA model is a strong solution with high accuracy for tackling the overbreak issues. The results of this study showed that the developed models can provide more accurate overbreak values compared to the intelligent techniques available in the literature; they can be used in practice and similar projects. [ABSTRACT FROM AUTHOR]