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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. [ABSTRACT FROM AUTHOR]

A vertex coloring of a graph G $G$ is called distinguishing if no nonidentity automorphisms of G $G$ can preserve it. The distinguishing number of G $G$, denoted by D(G) $D(G)$, is the minimum number of colors required for such a coloring, and the distinguishing threshold of G $G$, denoted by θ(G) $\theta (G)$, is the minimum number k $k$ such that every k $k$‐coloring of G $G$ is distinguishing. As an alternative definition, θ(G) $\theta (G)$ is one more than the maximum number of cycles in the cycle decomposition of automorphisms of G $G$. In this paper, we characterize θ(G) $\theta (G)$ when G $G$ is disconnected. Afterwards, we prove that, although for every positive integer k≠2 $k\ne 2$ there are infinitely many graphs whose distinguishing thresholds are equal to k $k$, we have θ(G)=2 $\theta (G)=2$ if and only if ∣V(G)∣=2 $| V(G)| =2$. Moreover, we show that if θ(G)=3 $\theta (G)=3$, then either G $G$ is isomorphic to one of the four graphs on three vertices or it is of order 2p $2p$, where p≠3,5 $p\ne 3,5$ is a prime number. Furthermore, we prove that θ(G)=D(G) $\theta (G)=D(G)$ if and only if G $G$ is asymmetric, Kn ${K}_{n}$ or Kn¯ $\bar{{K}_{n}}$. Finally, we consider all generalized Johnson graphs, J(n,k,i) $J(n,k,i)$, which are the graphs on all k $k$‐subsets of {1,...,n} $\{1,\ldots ,n\}$ where two vertices A $A$ and B $B$ are adjacent if ∣A∩B∣=k−i $| A\cap B| =k-i$. After studying their automorphism groups and distinguishing numbers, we calculate their distinguishing thresholds as θ(J(n,k,i))=nk−n−2k−1+1 $\theta (J(n,k,i))=\left(\genfrac{}{}{0ex}{}{n}{k}\right)-\left(\genfrac{}{}{0ex}{}{n-2}{k-1}\right)+1$, unless k=n2 $k=\frac{n}{2}$ and i∈{k2,k} $i\in \{\frac{k}{2},k\}$ in which case we have θ(J(n,k,i))=nk $\theta (J(n,k,i))=\left(\genfrac{}{}{0ex}{}{n}{k}\right)$. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Utilizing Distributed Generators (DGs) and Energy Storage Systems (ESSs) enhances power system reliability, drawing significant research attention. However, these systems pose challenges, compelling scientists to explore optimization methods. Our paper presents an innovative solution, Parallel Multi-Objective Multi-Verse Optimization (PMOMVO), aimed at optimizing DGs and Battery Energy Storage Systems (BESSs) allocation. This optimization addresses voltage violations and operation costs, crucial concerns for system operators and consumers. By leveraging a parallel approach, PMOMVO significantly accelerates the optimization process. We compared its results with a base case scenario, demonstrating the superior efficiency of our parallel method. It not only enhances the optimization performance but also proves its efficacy by generating optimal solutions from the Pareto front set. This research showcases the benefits of PMOMVO, offering a faster, more efficient, and reliable way to optimize power systems, benefiting both operators and consumers. • Introduces a novel bi-level optimization approach for DG and BESS allocation and operation. • Proposing a multi-objective formulation to identify Pareto optimal solutions, offering a balance between voltage stability and cost-effectiveness. • Optimizes asset operation strategies, considering both peak and off-peak load times, enhancing resource utilization. • Proposes a parallel processing method based on modified MOMVO, significantly accelerating computations for large distribution systems problems. • Achieves a substantial improvement in the speed-up index, ensuring swift and effective DG and BESS allocation and operation in complex systems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

An effective feature vector generation approach is herein presented based on the cointegration concept and the signal processing methods in order to improve varying speed bearing troubleshooting. Initially, each vibration signal is decomposed into its intrinsic mode functions (IMFs) using ensemble empirical mode decomposition method. Then, the cointegration relations are extracted by applying the Johansen trace test method to the obtained components. In order to form the feature matrix, the cointegration relations are analyzed by wavelet packet transform and the time-domain features are calculated for the wavelet packet coefficients. Consequently, by using a hybrid feature selection approach based on compensation distance evaluation technique, support vector machine, and binary particle swarm optimization algorithm, the optimal hybrid features were identified. The experimental results show that the optimal features are composed of the first cointegration relation, the third level of wavelet packet coefficients, the third IMF, Teager–Kaiser energy, energy, and root mean square. Also, the number of optimal features obtained from the first cointegration relation was less than other relationships. The results reveal that when the optimal feature set is computed for the first cointegration relationship, the identification accuracy of the proposed approach increases considerably. The result analysis also shows the efficiency of the proposed technique in detecting the condition of the bearing in time-varying rotational speed. [ABSTRACT FROM AUTHOR]

Centralized optimization methods have been widely used to manage the operation of distribution systems. However, these methods have some restrictions, such as the high computational cost, the reliance on a centralized computer system, and the lack of data privacy. To overcome these limitations, decentralized optimization methods have been proposed in recent years. Decentralized methods divide the control variables of the centralized optimization problem over several controllers, and each controller solves its own subproblem independently. This paper presents a multi-objective optimization framework for managing the operation of distribution systems. The primary objective of the proposed method is to optimize the tap positions of voltage regulators and charging and discharging powers for energy storage devices locally through a decentralized coordination process. The used objective functions take into account the voltage profile within the network, the lifetime of the devices, and the energy losses in the systems. Two decentralized methods, based on the Advanced Arithmetic Optimizer algorithm and the Profile Steering approach, are proposed to address the limitations of centralized optimization methods. The decentralized methods aim to improve the reliability and efficiency of the optimization process, while also minimizing communication and computational costs. The proposed methods are evaluated and compared to a centralized approach using the IEEE 33 and 69 bus systems. The results demonstrate that the proposed decentralized methods can effectively resolve voltage problems, minimize energy losses, and find high-quality solutions with improved computational efficiency compared to the centralized approach. • New objective function for voltage violations and asset lifetime maximization. • New objective function for minimizing energy losses and maximizing asset lifetime. • Investigation of decentralized methods for ESD operation with VRs to eliminate voltage deviations. • Investigating decentralized vs. centralized approach for optimizing objective function. • Development of AAO algorithm for MINLP with improved convergence and better solutions. [ABSTRACT FROM AUTHOR]

For any graph X with the adjacency matrix A , the transition matrix of the continuous-time quantum walk at time t is given by the matrix-valued function H X (t) = e i t A. We say that there is perfect state transfer in X from the vertex u to the vertex v at time τ if | H X (τ) u , v | = 1. It is an important problem to determine whether perfect state transfers can happen on a given family of graphs. In this paper we characterize all the graphs in the Johnson scheme which have this property. Indeed, we show that the Kneser graph K (2 k , k) is the only class in the scheme which admits perfect state transfers. We also show that, under some conditions, some of the unions of the graphs in the Johnson scheme admit perfect state transfer. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Excessive penetration of renewable energy resources into the distribution grid without additional preventive measures has led to several operational problems. However, most strategies developed to accommodate more renewable energy units suffered from other operational problems. Therefore, further efforts are needed to address the other key vulnerabilities of the grid in addition to maximizing the hosting capacity. In this regard, this study is devoted to a new multi-objective formulation to maximize the hosting capacity and minimize the total energy losses while satisfying the operational constraints and maximizing the energy transferred to off-peak hours. The Multi-Objective Advanced Gray Wolf Optimization (MOAGWO) algorithm is used as a solution tool. The proposed formulation and solution algorithm are tested on IEEE-33-bus and 69-bus medium voltage test systems. The impacts of energy storage systems, voltage regulators, and static var compensators on the hosting capacity and the objective functions are identified using several scenarios. The results showed that the optimal device type and locations depend on the level of DG penetration. Finally, a comparison according to two popular multi-objective performance indices showed that the quality of the Pareto front distribution obtained by MOAGWO was better than the ones obtained with the two other popular heuristic methods. • Maximization of the hosting capacity while minimizing the energy losses. • Maximization of the energy transferred to the off-peak hours through intelligent control. • Multi-Objective Advanced GWO algorithm to determine the Pareto front solutions. • Singular and collaborative impacts of the reinforcement units on the objectives. • High-quality Pareto solutions and high computational efficiency of MOAGWO. [ABSTRACT FROM AUTHOR]

In this article, a novel approach based on game theory is presented for multi-objective optimal synthesis of four-bar mechanisms. The multi-objective optimization problem is modelled as a Stackelberg game. The more important objective function, tracking error, is considered as the leader, and the other objective function, deviation of the transmission angle from 90° (TA), is considered as the follower. In a new approach, a group method of data handling (GMDH)-type neural network is also utilized to construct an approximate model for the rational reaction set (RRS) of the follower. Using the proposed game-theoretic approach, the multi-objective optimal synthesis of a four-bar mechanism is then cast into a single-objective optimal synthesis using the leader variables and the obtained RRS of the follower. The superiority of using the synergy game-theoretic method of Stackelberg with a GMDH-type neural network is demonstrated for two case studies on the synthesis of four-bar mechanisms. [ABSTRACT FROM AUTHOR]

A subset S of a group G ≤ Sym( n) is intersecting if for any pair of permutations $${\pi, \sigma \in S}$$ there is an $${i \in {1, 2, . . . , n}}$$ such that $${\pi (i) = \sigma (i)}$$ . It has been shown, using an algebraic approach, that the largest intersecting sets in each of Sym( n), Alt( n), and PGL(2, q) are exactly the cosets of the point-stabilizers. In this paper, we show how this approach can be applied more generally to many 2-transitive groups. We then apply this method to the Mathieu groups and to all 2-transitive groups with degree no more than 20. [ABSTRACT FROM AUTHOR]

The use of energy storage systems (ESS) and distributed generators (DGs) to improve reliability is one of the solutions that has received much attention from researchers today. In this study, we utilize a multi-objective optimization method for optimal planning of distributed generators in electric distribution networks from the perspective of multi-objective optimization. The objective is to improve the reliability of the network while reducing the annual cost and network losses. A modified version of the multi-objective sine–cosine algorithm is used to determine the optimal size, location, and type of DGs and the optimal capacity, location, and operation strategy of the ESS. Three case studies of IEEE 33-bus, 69-bus and 141-bus test systems with Turkish DG and load data were conducted to validate the effectiveness of the proposed approach. The distribution of the Pareto front solutions and the optimal objective functions are compared with the other known algorithms. The simulation results show that the average energy not supplied and annual energy losses for the test systems are reduced by up to 68% and 64%, respectively. Moreover, the Pareto fronts of the proposed method show a better distribution and dominate those obtained by MOGWO, MOSMA, NSGA-II, MOPSO and MOEA-D according to three different Pareto optimization metrics. Finally, the computational effort result shows faster convergence of MOSCA compared to MOGWO, MOSMA, NSGA-II, MOPSO and MOEAD. • Optimal DER planning and ESS management in EDNs using a three-dimensional multiobjective optimization framework • DN Reliability improvement together with power losses and annual cost minimization. • 72 h optimization period to account the seasonal characteristics of the load and the DGs. • Real load curve and wind and solar radiation data of Turkish territory. • Multi objective sine–cosine algorithm providing better Pareto fronts according to two performance indices. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy rule-based system (FRS) has been used for optimal reliability-based robust controller design for a two-mass-spring system with probabilistic uncertainties in its parameters. In this way, a multiobjective uniform-diversity genetic algorithm (MUGA) is first used to find a Pareto front of two-mass-spring system in a deterministic approach. This paper considers a two-mass-spring system under an impulse input. Two conflicting objective functions in this model include settling time of the second mass and control effort exerted on the first mass. Consequently, such Pareto front is then obtained for a two-mass-spring system with probabilistic uncertainties in its parameters using the probabilities of failure of those objective functions through a Monte Carlo simulation approach. It is shown that the FRS system removes the difficulty of selecting suitable crisp values and obligation due to a defining limit state function. Besides, the multiobjective Pareto optimization of such robust controllers using MUGA unveils some very important and informative trade-offs among those objective functions. Consequently, some optimum robust controllers can be compromisingly chosen from the Pareto frontiers. [ABSTRACT FROM PUBLISHER]

Abstract: A subset of the alternating group on points is intersecting if for any pair of permutations in , there is an element such that . We prove if and is intersecting, then . Also, we prove that provided that , then the only sets that meet this bound are the cosets of the stabilizer of a point of . These two results were first proven by Ku and Wong (2007), the proof given in this paper uses an algebraic method that is very different from the original proof. [Copyright &y& Elsevier]

• A new formulation comprising energy exchange between off-peak and peak load hours. • MOGOA comprising an additional stopping criteria. • Reduced Pareto solution space due to cost analysis at peak load hours. • Better performance than the other heuristic optimization algorithms. • Real load and DG data with site-specific constraints and incentives of Turkey. The penetration of small-scale generators (DGs) and battery energy storage systems (BESSs) into the distribution grid is growing rapidly and reaching a high percentage of installed generation capacity. These units can play a significant role in achieving various objectives if installed at suitable locations with appropriate sizes. In this paper, we present a new multi-objective optimization model to improve voltage profiles, minimize DG and BESS costs, and maximize energy transfer between off-peak and peak hours. We allocate and size DG and BESS units to achieve the first two objectives, while optimizing the operation strategy of BESS units for the last objective. The Multi-Objective Grasshopper Optimization Algorithm (MOGOA) is used to solve the formulated constrained optimization problem. The proposed formulation and solution algorithm are tested on 33-bus and 69-bus radial distribution networks. The advantages of the Pareto solutions are discussed from various aspects, and the Pareto solutions are subjected to cost analysis to identify the best solutions in the context of the worst voltage profiles at peak load times. Finally, the performance of the MOGOA algorithm is compared with the other heuristic optimization algorithms using two Pareto optimality indices. [ABSTRACT FROM AUTHOR]

A vertex coloring of a graph G is called distinguishing (or symmetry breaking) if no non-identity automorphism of G preserves it, and the distinguishing number, shown by D (G) , is the smallest number of colors required for such a coloring. This paper is about counting non-equivalent distinguishing colorings of graphs with k colors. A parameter, namely Φ k (G) , which is the number of non-equivalent distinguishing colorings of a graph G with at most k colors, is shown here to have an application in calculating the distinguishing number of the lexicographic product and the X -join of graphs. We study this index (and some other similar indices) which is generally difficult to calculate. Then, we show that if one knows the distinguishing threshold of a graph G , which is the smallest number of colors θ (G) so that, for k ≥ θ (G) , every k -coloring of G is distinguishing, then, in some special cases, counting the number of distinguishing colorings with k colors is very easy. We calculate θ (G) for some classes of graphs including the Kneser graph K (n , 2). We then turn to vertex partitioning by studying the distinguishing coloring partition of a graph G ; a partition of vertices of G which induces a distinguishing coloring for G. There, we introduce Ψ k (G) as the number of non-equivalent distinguishing coloring partitions with at most k cells, which is a generalization to its distinguishing coloring counterpart. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Geometrical imperfections and residual stresses are two inevitable aspects in studying of graphene sheets (GSs) which despite of their vital influence on the forced nonlinear vibrational behavior they have not been considered in previous research works. Hence, this work is an attempt to fill this important gap for the first time. Utilizing Eringen's nonlocal plate theory, assuming von Karman nonlinear terms in strain-displacement relations and employing calculus of variations, we have derived partial differential equations (PDEs) of the displacement field. The function of initial curvature was assumed as the first mode-shape of the simply-supported rectangular plates with a variable mode-coefficient. Next, Galerkin decomposition method has been used to achieve the equation of motion and for nonlinear vibration analysis time multiple scales method (TMS) was applied. The nonlinear frequency response relation has been analytically achieved. Afterwards, comparing to molecular dynamic (MD) results presented in previous researches we validated natural frequencies of single layer graphene sheets (SLGSs) in different sizes. Furthermore, nonlinear frequency response curves and time histories based on TMS, were in an excellent agreement with numerical method results. Therefore, the influences of external pressure, nonlocal parameter, geometric imperfection and residual stress have been presented comprehensively. It was observed that both imperfection and stress intensify the softening behavior in the system. Interestingly, for a certain controllable magnitude of the imperfection, it has been illustrated that the generated softening term counteracts with the hardening due to geometrical nonlinearity and consequently the nonlinear system behaves as a linear one. [ABSTRACT FROM AUTHOR]

• The periodicity intensity factor is used for detecting the bearing degradation starting point. • The new hybrid fault – sensitive feature extraction technique is introduced using signal processing methods. • The new hybrid feature is able to detect the moment of the fault occurrence in the run-to-failure test. In this article, a new approach has been presented in which novel hybrid features are introduced to detect the time of the beginning of the bearing degradation in the run-to-failure test. At first, the ensemble empirical mode decomposition (EEMD) method and the wavelet packet decomposition (WPD) are used in signal decomposition. The conventional frequency and time domain features, the envelope harmonic-to-noise ratio (EHNR) and the recently introduced periodicity intensity factor (PIF) are utilized for constructing the features matrix. Consequently, the most sensitive feature is identified using the compensation distance evaluation technique. The results show that the hybrid features obtained by the PIF, the EEMD and WPD methods can determine the exact moment of degradation. Also, the new hybrid features are superior to the features introduced in other studies in the early fault detection. [ABSTRACT FROM AUTHOR]