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For better estimation of renewable environmental friendly and carbon-free energy resources, precise prediction of solar energy is very essential. However, accurate prediction of solar energy is a challenging task due to its fluctuations and due to climatic factors those make it very nonlinear in nature. Therefore, in this study, the novel robust soft computing method is applied to predict solar​ radiation of two stations located in the southeast region of China. For modeling solar radiation of selected stations, the improved version of multi verse optimizer algorithm (IMVO) is utilized with integration of least square support vector machine (LSSVM) for better tuning the hyperparameters of LSSVM model. For validation, the newly developed method is compared with other algorithms integrated with LSSVM models, such as LSSVM with genetic algorithm (LSSVM-GE), LSSVM with gray wolf optimization (LSSVM-GWO), LSSVM with sine–cosine algorithm (LSSVM-CSA) and LSSVM with multi verse algorithm original version (LSSVM-MVO). It is found that newly developed method, LSSVM-IMVO, provided more accurate results in comparison to other models. For better visualization of data and model application, three different training testing data splitting strategies are used. It is found that the increase in training sample size considerably improved the models’ accuracies.

This study enhances the prediction of biochemical oxygen demand (BOD5), a vital water quality parameter, by developing hybrid artificial neural network models integrated with advanced optimization algorithms. Data from two monitoring stations in South Korea were used to create five models, including the innovative ANN-Enhanced Runge Kutta (ANN-ERUN) model. ANN-ERUN achieved the highest accuracy, significantly outperforming other models. At Gong station, it reduced prediction error (root mean square error: 1.24 mg/L; mean absolute error: 0.83 mg/L) and achieved a determination coefficient of 0.857. Models using eight water quality parameters, including dissolved oxygen and chemical oxygen demand, exhibited superior performance. These findings confirm the effectiveness of ANN-ERUN in precise BOD5 prediction, offering a robust tool for environmental monitoring and sustainable water quality management.

Feature selection (FS) methods are necessary to develop intelligent analysis tools that require data preprocessing and enhancing the performance of the machine learning algorithms. FS aims to maximize the classification accuracy by minimizing the number of selected features. This paper presents a new FS method using a modified Slime mould algorithm (SMA) based on the firefly algorithm (FA). In the developed SMAFA, FA is adopted to improve the exploration of SMA, since it has high ability to discover the feasible regions which have optima solution. This will lead to enhance the convergence by increasing the quality of the final output. SMAFA is evaluated using twenty UCI datasets and also with comprehensive comparisons to a number of the existing MH algorithms. To further assess the applicability of SMAFA, two high-dimensional datasets related to the QSAR modeling are used. Experimental results verified the promising performance of SMAFA using different performance measures.

Accurate solar radiation (SR) prediction is one of the essential prerequisites of harvesting solar energy. The current study proposed a novel intelligence model through hybridization of Adaptive Neuro-Fuzzy Inference System (ANFIS) with two metaheuristic optimization algorithms, Salp Swarm Algorithm (SSA) and Grasshopper Optimization Algorithm (GOA) (ANFIS-muSG) for global SR prediction at different locations of North Dakota, USA. The performance of the proposed ANFIS-muSG model was compared with classical ANFIS, ANFIS-GOA, ANFIS-SSA, ANFIS-Grey Wolf Optimizer (ANFIS-GWO), ANFIS-Particle Swarm Optimization (ANFIS-PSO), ANFIS-Genetic Algorithm (ANFIS-GA) and ANFIS-Dragonfly Algorithm (ANFIS-DA). Consistent maximum, mean and minimum air temperature data for nine years (2010–2018) were used to build the models. ANFIS-muSG showed 25.7%–54.8% higher performance accuracy in terms of root mean square error compared to other models at different locations of the study areas. The model developed in this study can be employed for SR prediction from temperature only. The results indicate the potential of hybridization of ANFIS with the metaheuristic optimization algorithms for improvement of prediction accuracy.

Oil production forecasting is an important task to manage petroleum reservoirs operations. In this study, a developed time series forecasting model is proposed for oil production using a new improved version of the adaptive neuro-fuzzy inference system (ANFIS). This model is improved by using an optimization algorithm, the slime mould algorithm (SMA). The SMA is a new algorithm that is applied for solving different optimization tasks. However, its search mechanism suffers from some limitations, for example, trapping at local optima. Thus, we modify the SMA using an intelligence search technique called opposition-based learning (OLB). The developed model, ANFIS-SMAOLB, is evaluated with different real-world oil production data collected from two oilfields in two different countries, Masila oilfield (Yemen) and Tahe oilfield (China). Furthermore, the evaluation of this model is considered with extensive comparisons to several methods, using several evaluation measures. The outcomes assessed the high ability of the developed ANFIS-SMAOLB as an efficient time series forecasting model that showed significant performance.

Friction stir welding (FSW) method becomes an effective technique for welding dissimilar alloys such as AA2024 and AA5083 as the conventional fusion welding methods are not very suitable for welding the dissimilar alloysand cause many defects during solidification process. Since the strength of FSWedjoints depends on the process parameters, a new method was used in this study to predict the tensile behavior of friction stir welded AA5083 and AA2024 aluminum alloy joints in terms of tensile elongation (TE) and ultimate tensile strength (UTS). A new metaheuristic algorithm called Marine Predators Algorithm (MPA) has been integrated with Random Vector Functional Link (RVFL) network to improve the prediction accuracy. Rotational speed, welding speed, tool axial force, and pin profile were used as input parameters while UTS and TE works as the corresponding outputs. The MPA-RVFL model was tested and validated,a great agreement was demonstrated between the experimental results and the predicted results indicating that the developed technique is accurate and reliable to predict the tensile behavior of welded aluminum joints.

Crucial for carbon capture, utilization, and storage (CCUS) initiatives and diverse industries, heat transfer underscores the need for a precise assessment of carbon dioxide (CO2) and nitrogen (N2) viscosities in gaseous blends across various temperatures. This research pioneers an intelligent model by enhancing the dendritic neural regression (DNR) framework, employing the Seagull Optimization Algorithm with Marine Predator Algorithm (SOAMPA) for optimal predictions. Leveraging recent advancements in metaheuristic optimization techniques, the study reveals the superior performance of the novel SOAMPA approach in predictive accuracy, marking a significant breakthrough in predicting CO2-N2mixture viscosities with implications for advancing CCUS projects and diverse industries. The optimized DNR model, empowered by the modified SOAMPA optimization technique, contributes to estimating the viscosity of N2-CO2mixture gases. Utilizing inputs like pressure, temperature, mole fraction of N2, and model fraction of CO2, the models are trained and tested on a dataset comprising over 3030 data samples from public literature. Key contributions encompass proposing an optimized DNR approach, introducing the modified SOAMPA technique, and demonstrating its superiority over established optimization methods in conjunction with the traditional DNR model for predicting viscosity based on real experimental datasets.

This paper provides an alternative method for creating an optimal subset from features which in turn represent the whole features through improving the moth-flame optimization (MFO) efficiency in searching for such optimal subset. The improvement is performed by combining the opposition-based learning technique and the differential evolution approach with the MFO. The opposition-based learning is used to generate an optimal initial population to improve the convergence of the MFO; meanwhile, the differential evolution is applied to improve the exploitation ability of the MFO. Therefore, the proposed method noted as OMFODE has the ability to avoid getting stuck in a local optimal value, unlike the traditional MFO algorithm and increase the fast convergence. The performance evaluation of our approach will be through a group of experimental results. In the first one, the proposed method has been tested over several CEC2005 benchmark functions. The second experimental series aims to assess the quality of the proposed method to improve the classification of ten UCI datasets by performing feature selection on such datasets. Another experiment is testing our method for classifying a real dataset, which represents some types of the galaxy images. The experimental results illustrated that the proposed algorithm is superior to the state-of-the-art meta-heuristic algorithms in terms of the performance measures. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is a machine learning process commonly used to reduce the high dimensionality problems of datasets. This task permits to extract the most representative information of high sized pools of data, reducing the computational effort in other tasks as classification. This article presents a hybrid optimization method for the FS problem; it combines the slap swarm algorithm (SSA) with the particle swarm optimization. The hybridization between both approaches creates an algorithm called SSAPSO, in which the efficacy of the exploration and the exploitation steps is improved. To verify the performance of the proposed algorithm, it is tested over two experimental series, in the first one, it is compared with other similar approaches using benchmark functions. Meanwhile, in the second set of experiments, the SSAPSO is used to determine the best set of features using different UCI datasets. Where the redundant or the confusing features are removed from the original dataset while keeping or yielding a better accuracy. The experimental results provide the evidence of the enhancement in the SSAPSO regarding the performance and the accuracy without affecting the computational effort.

Carbon dioxide (CO2) emission is an emergency issue in terms of environmental pollution. Estimation of carbon capture, utilization, and storage (CCUS) is a necessary task that received wide attention. Due to this fact, numerous studies proposed underground carbon storage to reduce CO2emissions in the atmosphere. However, there are some drawbacks about estimation accuracy trapping efficiency in deep saline aquifers. Also, the time computation of conventional reservoir simulators requires weeks or months to complete the simulation tasks. Hence, a new approach about accuracy and a fast predictive model needs to propose for promoting the application of carbon capture and storage projects. Therefore, this paper proposes an optimized Adaptive Neuro fuzzy inference system (ANFIS) to predict two indices of the CO2Trapping in deep saline aquifers, namely, solubility trapping index (STI) residual trapping index (RTI), using 6810 simulation samples, 8 input features of subsurface information from 33 fields of ten previous studies. We utilize the recently developed optimization algorithms, called Aquila optimizer (AO) and Salp Swarm Algorithm (SSA), to train the ANFIS model and to optimize its parameters to boost the prediction performance of the traditional ANFIS. The search mechanism of the SSA is used instead of the original one of the AO algorithm, which enhances the exploration process of the traditional AO. The proposed AOSSA-ANFIS is outperformed to seven optimized ANFIS models.

This paper presents an alternative method for predicting biochar yields from biomass thermochemical processes. As biochar is considered a renewable and sustainable energy source, it has received more attention. Several methods have been presented to predict biochar, such as neural network (NN) and least square support vector machine (LS-SVM). However, each of them has its own drawbacks, such as getting stuck in a local optimum, which occurs in NN, and lack of uncertainty and time complexity, as in LS-SVM. Therefore, this paper avoids this limitation by using a hybrid method between the adaptive neuro-fuzzy inference system (ANFIS) and gray wolf optimization (GWO) algorithm. The proposed method is called ANFIS-GWO, which consists of two stages. In the first stage, GWO is used to learn the parameters of ANFIS using the training set. Meanwhile, in the second stage, the testing set is used to evaluate the performance of the proposed ANFIS-GWO method. Three experiments were performed to assess the performance of the proposed method. The first experiment used a set of UCI (University of California, Irvine) benchmark datasets to evaluate the effectiveness of ANFIS-GWO. The aim of the second experiment was to evaluate the performance of the proposed ANFIS-GWO method to predict biochar yield from manure pyrolysis. The third experiment aimed to estimate the values of input parameters of pyrolysis that maximize biochar production. The obtained results were compared to those of other methods, such as ANFIS using gradient descent, practical swarm optimization, genetic algorithm, whale optimization algorithm, sine-cosine algorithm, and LS-SVM. The results of the ANFIS-GWO method were >35% of the standard ANFIS and also better than those of other methods.

In China, Tahe Triassic oil field block 9 reservoir was discovered in 2002 by drilling wells S95 and S100. The distribution of the reservoir sand body is not clear. Therefore, it is necessary to study and to predict oil production from this oil field. In this study, we propose an improved Random Vector Functional Link (RVFL) network to predict oil production from Tahe oil field in China. The Spherical Search Optimizer (SSO) is applied to optimize the RVFL and to enhance its performance, where SSO works as a local search method that improved the parameters of the RVFL. We used a historical dataset of this oil field from 2002 to 2014 collected by a local partner. Our proposed model, called SSO-RVFL, has been evaluated with extensive comparisons to several optimization methods. The outcomes showed that, SSO-RVFL achieved accurate predictions and the SSO outperformed several optimization methods.