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The widespread adoption of electric vehicles (EVs) poses challenges associated with charging infrastructures and their impact on the electrical grid. To address these challenges, smart charging approaches have emerged as a key solution that optimizes charging processes and contributes to a smarter and more efficient grid. This paper presents an innovative multi-objective optimization framework for EV smart charging (EVSC) using the Dynamic Hunting Leadership (DHL) method. The framework aims to improve the voltage profile of the system in addition to eliminating voltage violations and energy not supplied (ENS) to EVs within the network. The proposed approach considers both residential EV chargers and parking stations, incorporating realistic EV charger behaviors based on constant current charging and addressing the problem as a mixed integer non-linear programming (MINLP) problem. The performance of the optimization method is evaluated on a distribution network with varying levels of EV penetration connected to the chargers in the grid. The results demonstrate the effectiveness of the DHL algorithm in minimizing conflicting objectives and improving the grid’s voltage profile while considering operational constraints. This study provides a road map for EV aggregators and EV owners, guiding them on how to charge EVs based on preferences while minimizing adverse technical impacts on the grid.

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Dimensional synthesis of crank-rocker mechanisms applied to provide some desired values of stroke and time ratio, is of utmost importance for designing an efficient mechanism. In the synthesis and manufacturing of crank-rocker mechanisms, the designers are further challenged by other design criteria, such as quality of motion. In this study, a novel approach based on genetic programming (GP) is proposed for dimensional synthesis of planar crank-rocker mechanisms with optimum transmission angle over the desired stroke and time-ratio. An analytical approach is elaborated which leads to an interesting relationship of length of the coupler and rocker links. It is, therefore, advised that by adopting equal lengths for coupler link and rocker link, one can guarantee the optimality of the transmission angle’s deviation ( TA ), ensuring the Grashof condition. Consequently, through an inverse modeling approach, GP method is utilized to construct some explicitly mathematical formulas to represent all the sizes and dimensions of the crank-rocker mechanism based on the any desired values of both stroke and time-ratio. In this way, an input-output data set consisting of all the dimensions and the sizes of the links as the input variables and both the stroke and time-ratio as output variables is first constructed using the pertinent mathematical equations. Indeed, such approach of inverse modeling using GP simplifies greatly the synthesis of the crack-rocker mechanisms for any desired values of both stroke and time-ratio. The proposed approach has been applied for kinematic synthesis of a crank-rocker mechanism. A comparison between the obtained results of this work and the analytic method, clearly illustrates the efficiency of the proposed approach.

A significant challenge in troubleshooting rotary machines is the use of signal processing methods that are often hindered by mode mixing, end effects, and predetermined parameters. This paper introduces an Improved Swarm Decomposition (ISWD) method to address these limitations. ISWD is an enhancement of the Swarm Decomposition (SWD) method, which employs a swarm-based intelligence technique to decompose a signal into oscillatory components (OCs). The performance of the original SWD method is highly dependent on two threshold parameters. A new objective function incorporating kurtosis, cross-correlation, and an orthogonal index, is defined and minimized by PSOSCALF algorithm, a hybrid of Particle Swarm Optimization, Lévy flight, and the sine–cosine algorithm. The ISWD technique is evaluated using noisy verification signals of early bearing fault data and the results are compared with other signal processing methods. Two case studies involving defective inner and outer rings are considered. The results indicate that the characteristic frequencies of bearings and their harmonics in the envelope spectra of components obtained by ISWD are clearly revealed, enabling easy identification of bearing fault types. Furthermore, the components extracted by ISWD are physically meaningful and contain higher-quality information than those obtained by SWD and other methods.

The optimum penetration of distributed generations into the distribution grid provides several technical and economic benefits. However, the computational time required to solve the constrained optimization problems increases with the increasing network scale and may be too long for online implementations. This paper presents a parallel solution of a multi-objective distributed generation (DG) allocation and sizing problem to handle a large number of computations. The aim is to find the optimum number of processors in addition to energy loss and DG cost minimization. The proposed formulation is applied to a 33-bus test system, and the results are compared with themselves and with the base case operating conditions using the optimal values and three popular multi-objective optimization metrics. The results show that comparable solutions with high-efficiency values can be obtained up to a certain number of processors.

In this article, a Stackelberg game theoretic approach is used for multi-objective optimal synthesis of four-bar mechanisms for path generation. Two objective functions, namely, tracking error (TE) and transmission angle’s deviation from 90° (TA), are considered as leader and follower, respectively. In this way, a new analytical approach is presented to provide a function of rational reaction set (RRS) of the follower. Consequently, the multi-objective optimal synthesis of four-bar mechanism is therefore cast into a single-objective optimal synthesis using only the leader variables and the obtained nonlinear algebraic equation representing the RRS of the follower. The superiority of using such game theoretic method of Stackelberg is demonstrated for three cases of synthesis of four-bar mechanisms for path generation.

The integration of distributed generators (DGs), which are based on renewable energy sources, energy storage systems, and static VAR compensators (SVCs), requires considering more challenging operational cases due to the variability of DG production contributed by different characteristics for different time sequences. The size, quantity, technology, and location of DG units have major effects on the system to benefit from the integration. All these aspects create a multi-objective scope; therefore, it is considered a multi-objective mixed-integer optimization problem. This paper presents an improved multi-objective salp swarm optimization algorithm (MOSSA) to obtain multiple Pareto efficient solutions for the optimal number, location, and capacity of DGs and the controlling strategy of SVC a radial distribution system. MOSSA is a bio-inspired optimizer based on swarm intelligence techniques and it is used in finding the optimal solution for a global optimization problem. Two sets of objective functions have been formulated minimizing DGs and SVC cost, voltage violation, energy losses, and system emission cost. The usefulness of the proposed MOSSA has been tested with the 33-bus and 141-bus radial distribution systems and the qualitative comparisons against two well-known algorithms, multiple objective evolutionary algorithms based on decomposition (MOEA/D), and multiple objective particle swarm optimization (MOPSO) algorithm.

Due to the complex mathematical structures of the models in engineering, heuristic methods which do not require derivative are developed. This paper improves recently developed Grey Wolf Optimization Algorithm by extending it with three new features: namely presenting a new formulation for evaluating the positions of search agents, applying mirroring distance to the variables violating the limits, and proposing a dynamic decision approach for each agent either in exploration or exploitation phases. The performance of Advanced Grey Wolf Optimization (AGWO) method is tested using several optimization test functions and compared to several heuristic algorithms. Moreover, a planning problem in smart grids is solved by considering different objective functions using 33 and 141 bus distribution test systems. From the numerical simulation results, we observe that, AGWO is able to find the best results compared to other methods from 10 and 9 out of 13 test functions for 30 and 60 variables, respectively. Similar to this, it finds best function values for 5 out of 10 fixed number of variable test functions. Also, the result of the CEC-C06 2019 benchmark functions shows that AGWO outperforms 8 for optimization problems from 10. In power distribution system planning problem, better objective function values were determined by using AGWO, resulting a better voltage profile, less losses, and less emission costs compared to solutions obtained by Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms.

There has been in recent years a trend towards adopting deep neural networks for addressing earth science problems. Of the various deep neural networks applied to different problems in earth sciences, this study aimed to demonstrate how to apply the group method of data handling (GMDH) neural networks for mineral prospectivity mapping (MPM). GMDH neural networks are sophisticated, multilayered, robust tools for addressing complex regression problems. However, labeled data for MPM, which constitute multivariate attributes of known deposit and non-deposit sites, are often (if not always) insufficient to train neural networks adequately. Such issue triggers networks' poor generalization; that is, the networks developed fit the labeled data perfectly (i.e., low bias) but cannot predict unseen data accurately (i.e., high variance). Given this, GMDH neural networks were, in this study, coupled with a window-based data augmentation technique, an approach to generate additional geologically constrained labeled samples for MPM, and applied to a district hosting few but giant porphyry copper deposits. It was recognized that coupling the data augmentation technique with GMDH neural networks yielded robust predictive models that can handle the bias–variance tradeoff, making this combined methodology a viable option for MPM in terrains that host few but large mineral deposits.

The quality of information extracted from the vibration signals, and the accuracy of the bearing status detection depend on the methods used to process the signal and select the informative features. In this paper, a new hybrid approach is introduced in which the relatively new swarm decomposition (SWD) method and the optimized compensation distance evaluation technique (OCDET) are used to enhance the signal processing stage and to improve the optimal features selection process, respectively. Firstly, the vibration signals are decomposed into their Oscillatory Components (OCs) using the SWD. The feature matrix is constructed by computing the time-domain features for the OCs. The CDET method is consequently utilized to select the most sensitive features corresponding to the bearing status. On the other hand, The CDET approach contains a parameter called threshold which affects the number of the selected features. In this way, the hybrid optimization algorithm, which is a combination of the Particle Swarm Optimization (PSO) algorithm with the Sine–Cosine Algorithm (SCA) and the Levy flight distribution, has been used to select the optimal CDET threshold and improve the support vector machine (SVM) classifier. The proposed technique ability is evaluated by vibration signals corresponding to different bearing defects and various speeds. The results indicate the capability of the proposed fault diagnosis method in identifying the very small-size defects under various bearing conditions. Finally, the presented method shows better performance in comparison with other well-known methods in the most of the case studies.