The main advantage of heuristic or metaheuristic algorithms compared to exact optimization methods is their ability in handling large-scale instances within a reasonable time, albeit at the expense of losing a guarantee for achieving the optimal solution. Therefore, metaheuristic techniques are appropriate choices for solving NP-hard problems to near optimality. Since the Parameters of heuristic and metaheuristic algorithms have a great influence on their effectiveness and efficiency, Parameter Tuning and calibration has gained importance. In this paper a new approach for robust Parameter Tuning of heuristics and metaheuristics is proposed, which is based on a combination of Design of Experiments (DOE), Signal to Noise (S/N) ratio, Shannon entropy, and VIKOR methods, which not only considers the solution quality or the number of fitness function evaluations, but also aims to minimize the running time. In order to evaluate the performance of the suggested approach, a computational analysis has been performed on the Simulated Annealing (SA) and Genetic Algorithms (GA) methods, which have been successfully applied in solving respectively the n-queens and the Uncapacitated Single Allocation Hub Location combinatorial problems. Extensive experimental results showed that by using the presented approach the average number of iterations and the average running time of the SA were respectively improved 12 and 10.2 times compared to the un-tuned SA. Also, the quality of certain solutions was improved in the tuned GA, while the average running time was 2.5 times faster compared to the un-tuned GA.

A novel hybrid intelligent approach for Tuning the Parameters of Interval Type-2 Intuitionistic Fuzzy Logic System (IT2IFLS) is introduced for the modeling and prediction of coronavirus disease 2019 (COVID-19) time series. COVID-19 is known to be a virus caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARSCoV-2) with a huge negative impact on human, work and world economy. Globally, more than 100 million people have been infected with over two million deaths and it is not certain when the pandemic will end. Predicting the trend of the COVID-19 therefore becomes an important and challenging task. Many approaches ranging from statistical approaches to machine learning methods have been formulated and applied for the prediction of the disease. In this work, the sliding mode control learning algorithm is used to adjust the Parameters of the antecedent parts of  IT2IFLS system while the gradient descent backpropagation is adopted to tune the consequent Parameters in a hybrid manner. The results of the hybrid intelligent learning model are compared with results of single learning models using sliding mode control and gradient descent algorithms and found to provide good performance in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) especially in noisy environments. The type-2 hybrid model also outperforms its type-1 counterparts in the different problem instances.

Vehicle routing in last-mile delivery plays a decisive role in the new world of people’s lifestyles. At present, a growing number of people order their needs online, and this forces companies  to employ innovative delivery logistics to reduce their last-mile shipping costs. The goal is to minimize the cost of travel that depends on the Euclidean distance between customers. Companies require solving vehicle routing problems (VRP) in a reasonable time. In this paper, a new approach is introduced that solves the multi-depot vehicle routing problem (MDVRP) in real-time. We propose a new method by clustering and decomposing the main problem into smaller ones using a Tuning Parameter α . This approach could reduce the solution time noticeably (up to 95%) while the shipping cost is still reasonable.

The conjecture of stock exchange is the demonstration of attempting to decide the forecast estimation of a particular sector or the market, or the market as a whole. Every stock every investor needs to foresee the future evaluation of stocks, so a predicted forecast of a stock’ s future cost could return enormous benefit. To increase the accuracy of the Conjecture of stock Exchange with daily changes in the market value is a bottleneck task. The existing stock market prediction focused on forecasting the regular stock market by using various machine learning algorithms and in-depth methodologies. The proposed work we have implemented describes the new NN model with the help of different learning techniques like hyperParameter Tuning which includes batch normalization and fitting it with the help of random-search-cv. The prediction of the Stock exchange is an active area for research and completion in Numerai. The Numerai is the most robust data science competition for stock market prediction. Numerai provides weekly new datasets to mold the most exceptional prediction model. The dataset has 310 features, and the entries are more than 100000 per week. Our proposed new neural network model gives accuracy is closely 86%. The critical point, it isn’ t easy with our proposed model with existing models because we are training and testing the proposed model with a new unlabeled dataset every week. Our ultimate aim for participating in Numerai competition is to suggest a neural network methodology to forecast the stock exchange independent of datasets with reasonable accuracy.

This paper presents a design procedure and a new control method for power regulation of series resonant Induction Heating (IH) systems using a self-oscillating Tuning loop. The proposed power regulator can accurately estimate the instantaneous phase angle and the main Parameters of the resonant load. Moreover, the power control algorithm is devised based on a combination of Phase Shift (PS) and Pulse Density Modulation (PDM) methods. For simplicity, the Tuning loop utilizes the PS control method for power regulation. Moreover, the Pulse Density Modulation (PDM) and frequency-sweep methods can be used in the proposed Tuning loop. The new method is verified by a laboratory prototype with an output power of about 220 W and an operating frequency of about 60 kHz.

In this paper, a new method is presented to improve the dynamic stability of the power systems using unified power flow controller (UPFC). In this method, an adaptive particle swarm optimization (PSO) algorithm based on new acceleration coefficients (NAC-PSO) is proposed for solving optimization problems and Tuning the controller Parameters. The performance of the proposed algorithm is compared with other methods. The simulation results show that the Lyapunov controllers designed using NAC-PSO performed better than controllers designed by other methods. This method guarantees the stability of the power system against the Parameters and topology changes. Simulation results for a single-machine infinite-bus (SMIB) and multi-machine power system (IEEE 9-bus) show the effectiveness of the proposed method under small-signal conditions.