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Cognitive radio (CR) is the trending domain in addressing the inadequate bands for communication, and spectrum sensing is the hectic challenge need to be addressed extensively. In the conventional CRs, the communication is restricted to the secondary users (SUs) in the allocated bands causing the underutilization of the available band. Thus, with the aim to afford higher throughput and spectrum efficiency, this paper introduces the hybrid mixture model for spectrum sensing in the multiple-input–multiple-output (MIMO) systems and the effectiveness is evaluated based on the evaluation parameters, such as detection probability and probability of false alarm. The signal received through the orthogonal frequency-division multiplexing (OFDM) antenna is employed for analyzing the spectral availability for which the energy and Eigen statistics of the signal is generated, which forms the input to the Hybrid mixture model. The developed Hybrid mixture model is the integration of the Gaussian Mixture Model (GMM) and Whale Elephant-Herd Optimization (WEHO). The GMM is subjected to the optimal tuning using the WEHO, which is the modification of the standard Whale Optimization Algorithm (WOA) with the Elephant-Herd Optimization (EHO). The analysis reveals that the proposed spectrum sensing model acquired the maximal detection probability and minimal false alarm probability of 99.9% and 46.4%, respectively. The proposed hybrid mixture model derives the spectrum availability and ensures the effective communication in CR without any interference.

The NP-hard problem of scheduling jobs on unrelated parallel machines with the objective of minimizing the makespan is addressed. A variable neighborhood descent heuristic hybridized with mathematical programming is proposed. A new constructive heuristic is also introduced to seed the search with a solution residing in a promising region. The strength of the approach lies in the adoption of appropriately chosen optimization criteria that avoid early local optimum entrapment and in the hybridization of the heuristic with mathematical programming for the exploration of the large neighborhood structures. Experimental results on a large set of benchmark problem instances attest to the efficacy of the proposed approach.

One of the most widely used 3D printing techniques is fused deposition modeling (FDM) which builds things layer by layer by dispensing molten materials through a heated nozzle. To showcase the flexibility of 3D printing, test samples were made using polylactic acid (PLA). Trial runs with Taguchi’s (L27) orthogonal array were performed. Layer thickness, printing speed, and carbon deposition (C-deposition) were the three input parameters that were improved. This was accomplished by combining principal component analysis (PCA) with multi-objective optimization based on ratio analysis (MOORA) in an integrated approach to multi-criteria optimization. The main goal of this study is to maximize the input parameters for the Industry 4.0 technological production process of embossing components. The MOORA-PCA technique finds the best combinations of process variables; for example, printing at a speed of 75mm/s, layer thickness of 0.1mm, and carbon content of 15mg yield the required results. The results of this study will help production managers and researchers choose the best FDM 3D printing methods for improving mechanical qualities and surface roughness. The economic feasibility of the 3D printing businesses may be strengthened by these research findings, which will benefit customers looking for ecologically friendly items.

The e-learning platform gains significant attraction in the current scenario due to the outbreak of the epidemic COVID-19 as e-learning ensures the students continue their studies in the safest environment while maintaining the educational standard. The performance prediction is one of the significant tasks to be carried out in the e-learning platform to sort out the students who require immediate attention to enhance their grades before the final assessment. This paper proposes a prediction model that effectively predicts the learners’ performance in the e-khool learning management system (e-khool LMS) based on the proposed wolf-swarm optimization dependent Deep Long Short-term (wolf-swarm optimization-based Deep-LSTM) approach. The optimization algorithm tunes the optimal weights of the Deep-LSTM classifier, which inherits the hybrid characteristics of the traitors and particles. Initially, the learner data from the e-khool database is employed for classification based on the proposed wolf-swarm optimization dependent Deep-LSTM classifier. The effectiveness of the proposed prediction model is analyzed in terms of MSE and RMSE with the value of 5.93 and 2.426, respectively.

Bacterial foraging algorithm (BFA) is a population-based stochastic search technique for solving various scientific and engineering problems. However, it is inefficient in some practical situations. In order to improve the performance of the BFA, we propose a novel optimization algorithm, named quantum inspired bacterial foraging algorithm (QBFA), which applies several quantum computing principles, and a new mechanism is proposed to encode and observe the population. The algorithm has been evaluated on the standard high-dimensional benchmark functions in comparison with GA, PSO, GSO and FBSA, respectively. The proposed algorithm is then used to tune a PID controller of an automatic voltage regulator (AVR) system. Simulation results clearly illustrate that the proposed approach is very efficient and could be easily extended to 300 or higher-dimensional problems.

Accurate forecasting of rainfall has been one of the most important issues in hydrological research such as river training works and design of flood warning systems. Support vector regression (SVR) is a popular regression method in rainfall forecasting. Type of kernel function and kernel parameter setting in the SVR traing procedure, along with the input feature subset selection, significantly influence regression accuracy. In this paper, an effective hybrid optimization strategy by combining the strengths of genetic algorithm (GA) and simulated annealing (SA), is employed to simultaneously optimize the input feature subset selection, the type of kernel function and the kernel parameter setting of SVR, namely GASA–SVR. The developed GASA–SVR model is being applied for monthly rainfall forecasting in Guilin of Guangxi. The GA is carried out as a main frame of this hybrid algorithm while SA is used as a local search strategy to help GA jump out of local optima and avoid sinking into the local optimal solution early. Compared with SVR, pure GA–SVR and HGA–SVR, results show that the hybrid GASA–SVR model can correctly select the discriminating input features subset, successfully identify the optimal type of kernel function and all the optimal values of the parameters of SVR with the lowest prediction error values in rainfall forecasting, can also significantly improve the rainfall forecasting accuracy. Experimental results reveal that the predictions using the proposed approach are consistently better than those obtained using the other methods presented in this study in terms of the same measurements. Those results show that the proposed GASA–SVR model provides a promising alternative to monthly rainfall prediction.

In cloud computing, load balancing is crucial for effective resource management. Keeping servers from getting overworked entails dividing up incoming network traffic or computational tasks among several servers. Better resource management, increased throughput, and quicker reaction times result from this. Several heuristic and metaheuristic techniques have been used to disperse the load across the available virtual machines. Researchers have worked very hard to find a solution for the load balancing issue. This paper uses the following stages to develop a novel load-balancing model with optimization assistance: Virtual Machine (VM) classification, load balancing, and replica management are the three main processes. For the VM classification process, a modified version of the fuzzy clustering approach is suggested. For the load balancing procedure, the COOT Insisted Bald Eagle Search (COOTIBES) model is suggested. This optimization-assisted load balancing takes into account a number of limitations, including frequency, makespan, memory usage, resource utilization, and execution time. Additionally, the suggested COOTIBES algorithm manages replicas while taking load, put cost, and storage cost into account. Lastly, using various performance indicators, the suggested work’s performance is contrasted with traditional models. While the Inquisitive Genetic Algorithm with Grey Wolf Optimization Algorithm (IG-GWO), Ant Colony Optimization for Continuous Domains (ACOR), Life Choice-Based Optimizer (LCBO), Cat Swarm Optimization (CSO), Genetic Algorithm Combined with First Come First Serve + Genetic Algorithm Combined with Round Robin (GA-FCFS$+$GA-RR), Jellyfish Optimization (JFO), and Namib Beetle Optimization (NBO) offer bigger makespan values, the COOTIBES scheme gives a smaller makespan of 180 for a task count of 2000.

In this paper, an effective hybrid optimization strategy is employed to simultaneously optimize the type of kernel function and the kernel parameter setting of Support Vector Regression (SVR), namely SVR-HPSOSA. This hybrid algorithm focuses on combining the advantages of PSO (fast calculation and easy mechanism) and SA (ability to jump away from local optimum solutions and converge to the global optimum solution). The HPSOSA algorithm combined the metropolis processes of SA into the movement mechanism and parallel processing of PSO. By combining the two methods, the PSO-SA algorithm has the advantage of both fast calculation and searching in the direction of the global optimum solution, helping PSO jump out of local optima, avoiding into the local optimal solution early and leading to a good solution quality. Compared with SVRHPSOSA and pure SVR, results show that the SVR-HPSOSA model can successfully identify the optimal type of kernel function and all the optimal values of the parameters of SVR with the lowest prediction error values in rainfall-runoff forecasting, can significantly improve the rainfall-runoff forecasting accuracy. Experimental results reveal that the predictions using the proposed approach are consistently better than those obtained using the other methods presented in this study in terms of the same measurements.