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This work proposes a novel technique to conduct back‐analysis of lateral displacement of deep cement mixing (DCM) columns in deep excavation construction. For the first time, we propose a process to investigate both soil and underground structure end‐to‐end automatically. The novel technique is a complex combination of three crucial factors: (1) a nature‐inspired optimization algorithm (O), (2) a three‐dimensional PLAXIS Geotechnical Engineering software (P) and (3) a modern Python language programming (P), hereinafter called the a nature‐inspired optimization algorithm (O), a three‐dimensional PLAXIS Geotechnical Engineering software (P) and a modern Python language programming (P) (OPP) technique. The novel meta‐heuristic algorithm simulated the co‐evolved partnership behavior of shrimps and goby fishes, termed Shrimp and Goby Association (SGA), which plays an important role in complex analyses. A series of exams to determine the SGA's performance is conducted on 38 benchmark test functions (IEEE Congress on Evolutionary Computation 2017 and 2019) and three real‐world engineering design problems to showcase its applicability. The metaheuristic and PLAXIS 3D analysis work well together, which makes the back‐analysis technique powerfully to determinate stiffness parameters instead of the traditional approaches. Based on the optimized parameters, the lateral deflection of DCM and soil are well predicted for excavation. This study proposes a technique to estimate efficiently the stiffness parameter for very soft soil. As a consequence of the optimization process, an equation to determine the stiffness parameter of DCM columns from laboratory test is also proposed. Based on the obtained results, this research provides a comprehensive methodology for predicting risk, enhancing safety, saving time and money, and effectively designing and constructing underground structures. [ABSTRACT FROM AUTHOR]

The pre‐insertion resistors (PIR) within high‐voltage circuit breakers are critical components and warm up by generating Joule heat when an electric current flows through them. Elevated temperature can lead to temporary closure failure and, in severe cases, the rupture of PIR. To accurately predict the temperature of PIR, this study combines finite element simulation techniques with Support Vector Regression (SVR) optimized by an Improved Whale Optimization Algorithm (IWOA) approach. The IWOA includes Tent mapping, a convergence factor based on the sigmoid function, and the Ornstein–Uhlenbeck variation strategy. The IWOA‐SVR model is compared with the SSA‐SVR and WOA‐SVR. The results reveal that the prediction accuracies of the IWOA‐SVR model were 90.2% and 81.5% (above 100°C) in the ± 3°C temperature deviation range and 96.3% and 93.4% (above 100°C) in the ± 4°C temperature deviation range, surpassing the performance of the comparative models. This research demonstrates that the method proposed can realize the online monitoring of the temperature of the PIR, which can effectively prevent thermal faults PIR and provide a basis for the opening and closing of the circuit breaker within a short period. [ABSTRACT FROM AUTHOR]

Summary: To ensure smooth power exchange in a three‐phase inverter, it is important to accurately detect the faults of insulated gate bipolar transistor (IGBT) switches. However, the fault characteristics of power tubes are complex, the fault diagnosis results are affected by the load, and the recognition accuracy of some fault diagnosis models is low. To tackle this issue, this paper proposes an inverter open circuit fault diagnosis method named SR‐WOA‐ELM, which integrates signal reconstruction (SR), the whale optimization algorithm (WOA), and an extreme learning machine (ELM). First, the three‐phase output current is processed by the signal reconstruction method to eliminate the effect of load fluctuation, the average value of low frequency coefficients and wavelet entropy is extracted from the reconstructed three‐phase currents using the improved wavelet packet transform, and the fault feature vector is constructed by fusing the above two feature parameters. Second, the parameters of the ELM fault diagnosis model are optimized using WOA. Finally, the fault feature vector is added in the SR‐WOA‐ELM model to obtain the fault diagnosis results. Through simulation and experimental verification, the model can accurately identify the single‐tube and double‐tube faults of the inverter and is not affected by load fluctuations. [ABSTRACT FROM AUTHOR]

Using Hybrid optimization algorithms for nonlinear systems analysis is a novel approach. It is a powerful technique that uses the exploitation ability of one algorithm and the exploration ability of another algorithm, to find the best solution. Literature survey reveals that hybrid algorithms not only show quality response but also give faster convergence of error for nonlinear systems. In this paper, hybrid optimization techniques based proportional integral derivative (PID) controller is used for benchmark problems: Continuous stirred tank reactor (CSTR), Inverted pendulum and blood glucose system. Two recent hybrid algorithms: Particle swarm optimization‐Gravitational search algorithm (PSO‐GSA) and Particle swarm optimization‐Grey wolf algorithm (PSO‐GWO) are implemented to control the temperature and concentration of CSTR, pendulum angle of inverted pendulum, glucose concentration and insulin level of blood glucose system. In PID and PSOGWO algorithms, the exploration abilities of GSA and GWO combined with the exploitation ability of PSO have been used. The performance of these algorithms is then compared with individual PSO, GSA, and GWO algorithms proving their superiority. Stability is ensured using the Lyapunov approach while the robustness of the systems is checked using the parameter perturbation technique. Simulation results show substantial improvement in the performance of these systems by using these meta‐heuristic hybrid optimization techniques. A comparative analysis of these algorithms has also been done. [ABSTRACT FROM AUTHOR]

The aim of a Green Vehicle Routing Problem is to find an optimal route for alternative fuel vehicles to minimize the overall travelling distances while reducing energy consumption and CO2emissions. In this problem each vehicle handles a subset of customers/orders, leaving from and returning to the depot, with respect to maximum distance travelled, while minimizing energy consumptions. We proposed a time window‐based GVRP solution (GVRP‐TW) with exact routing approach. Wherein, each route serves a subset of customers/orders with minimum intermediate refuelling/recharges. To test our method, we conducted experiments using MATLAB software on a set of three reference problem specimens (C‐101, R‐101 and RC‐11). Our approach, evaluated on these benchmark problems, par exceeds in performance in relation to the existing VRP methods (like tabu search, variable neighbourhood search, and GRASP). Moreover, the proposed GVRP‐TW can be further optimized to solve other VRP problems with energy minimisations and less intermediate stops. [ABSTRACT FROM AUTHOR]

Summary: The investigations related to the planar antenna array have attracted much attention due to their vast applications in the areas of advanced wireless communication and electromagnetics. This article presents an effective synthesis method of a sparsely thinned symmetric planar antenna array using three well‐known meta‐heuristics including symbiotic organism search (SOS) algorithm, moth fly optimization (MFO), and multi‐verse optimization (MVO) algorithms. The main aim of this work is to optimize the positions of the switched‐on antenna elements on the array aperture in order to reduce the value of side‐lobe levels in the radiation field pattern in multiple planes for a desired first null beam width and subsequently to obtain the maximum reduced number of array‐elements in the antenna array. Two different cases are performed to optimize the radiation pattern in different azimuth angles with two different examples. The proposed methods can constrain the total number of array elements, inter‐element distance, and aperture area of the array. The radiation pattern characteristic and computation time linked with each example and each algorithm are recorded and compared with each other as well as with a fully populated planar symmetric rectangular array antenna of same aperture size for arriving at the conclusion. The simulation‐based results demonstrate the effectiveness of the proposed design and the efficiency of the performance using the SOS algorithm. [ABSTRACT FROM AUTHOR]

Summary: This article introduces Crystal Structure Algorithm (CryStAl), a novel metaheuristic optimization algorithm, for fine‐tuning microstrip patch antennas (MPA) in C‐band applications, which utilizes CryStAl with dielectric constant and resonant frequency inputs to optimize MPA dimensions, addressing limited bandwidth and gain issues. Achieves superior antenna performance with 32.71 dB return loss, 5.09 dB gain, and 1.2 VSWR, demonstrating improved convergence speed, radiation pattern, and suitability for diverse C‐band conditions. [ABSTRACT FROM AUTHOR]

Precisely locating the voltage sag sources is significant for improving power quality and clarifying the responsibilities of both power supply and consumption. However, the existing location models are complex, computationally intensive, and influenced by the measurement error and fault type, which have low positioning efficiency and large errors. Therefore, this paper proposes a method for locating voltage sag sources based on power polarity and twin characteristics matching. The method firstly uses the polarity of the sequence sag component disturbance reactive power at the monitoring point to determine the sag line. Secondly, the basic realization method of the twin power system is discussed, and the twin characteristics are defined by combining the positive and negative sequence sag component disturbance reactive power and the twin database is established. Thirdly, the spectral norm and Pearson product‐moment correlation coefficient are used to define the data matching degree, and the precise location model of the sag source is established with the objective function of maximizing the data matching between the twin characteristics of the preset and actual sag. Finally, the Improved Whale Optimization Algorithm is used to solve the location model. The correctness and superiority of the proposed method is verified based on IEEE33 node modelling. [ABSTRACT FROM AUTHOR]

This paper presents an enhanced whale optimization algorithm based on virtual force to optimize coverage and address the problem of uneven coverage during the deployment of underwater sensor networks. This method is guided by the Lévy flight and virtual force algorithms and adopts a differential mutation strategy based on random walks. This improves the fitness of the initial population and population richness of the algorithm. The effectiveness of this method was verified through simulation. [ABSTRACT FROM AUTHOR]

This study elucidates the use of optimization algorithms to identify the controller parameters employed in adjusting the current and voltage values of loads powered by solar energy systems and battery groups. Parameters for these controllers were independently derived using a combination of ant colony optimization with Levy flight, hybrid firefly‐particle swarm optimization, hybrid gravitation search algorithm‐particle swarm optimization, alongside the implementation of Jaya and whale optimization algorithms. The results from each method were juxtaposed for thorough analysis. In addition, three distinct Maximum Power Point Tracker (MPPT) algorithms were employed in the system: perturbation and observation, open circuit voltage, and incremental conductance (IC). To assess the system's adaptability to real‐world conditions, it was tested against varying temperatures and sunlight levels. Moreover, potential changes in the loads were considered by varying the load. The efficacy of the controllers was examined by altering both the environment and load. The effectiveness of the controllers was examined by referring to the integral of time‐weighted absolute error value. The system was simulated using MATLAB/Simulink software. This study demonstrates that the fractional‐order PID controller achieves the most effective results, the Jaya algorithm provides the best controller parameters, and the IC technique exhibits the highest performance in MPPT. [ABSTRACT FROM AUTHOR]

Built by machine‐learning, the surrogate model of metasurfaces reduces the need for a huge number of simulations in the design process, enhancing the efficiency and performance of the designed meta‐devices. However, the surrogate model of metasurfaces is often constructed‐based on specific physical perspectives or experiences, which limits its versatility. In this study, a generalized surrogate meta‐atom model for metasurfaces is introduced. This model can simulate arbitrary meta‐atoms and their corresponding electromagnetic responses at any polarization within the full space of pixelated unit cells. Utilizing a genetic algorithm, the model is employed to design various types of meta‐devices, automatically generating configurations of meta‐atoms with optimal performance for specific application scenarios. Three typical meta‐devices, including the reflective linear‐circular polarization converter, the metasurface‐based absorber, and the asymmetrical transmission meta‐slab, are designed and validated through full‐wave simulations and/or experiments. This work presents an efficient and flexible approach to model arbitrary metasurfaces, opening new possibilities for metasurface design and applications. [ABSTRACT FROM AUTHOR]

This paper addresses optimization problems subject to an objective function's evaluation‐number constraint arbitrarily given in advance. These problems are practical for black‐box objective functions which cost much money and/or time per evaluation because money and/or time constraints are typically applicable in real‐world projects. This paper suggests a new solution strategy that can be used to adapt metaheuristics to evaluation‐number constraints arbitrarily given in advance. The proposed strategy updates the setting parameters of a target metaheuristic algorithm for every generation by solving a fixed small‐size optimization problem related to the evaluation‐number constraint. It has the advantage of being insensitive to the effects of dimensionality increases in principle. The effectiveness of the proposed strategy is confirmed by numerical experiments with two well‐known metaheuristics, i.e., the particle swarm optimization algorithm and the differential evolution algorithm. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This study seeks to present a sophisticated artificial intelligence (AI) framework to model the compressive strength (fc′) of concrete containing waste foundry sand (WFS), with the aim of minimizing the need for time‐consuming laboratory tests and skilled technicians. For this purpose, artificial neural network (ANN) is hybridized with two metaheuristic algorithms—particle swarm optimization (PSO) and ant colony optimization (ACO) to predict the fc′ of 340 samples containing WFS collected from the literature. Results indicated that the ACO + ANN model showed the best performance with the Pearson coefficient of 0.9971, mean absolute error of 0.0221 MPa, and root mean squared error of 0.7473 MPa. The values of prediction errors exhibited that more than 90% of them in the ACO + ANN model fall within the range of (−1.5 MPa, 1.5 MPa), while this range for the PSO + ANN and traditional ANN models was obtained as (−3 MPa, 3 MPa) and (−4 MPa, 4 MPa), respectively. Furthermore, the proposed ACO + ANN model predicted the fc′ in the range of 5.24–54.48 MPa. Besides, the results indicated that the water‐to‐cement ratio, cement strength class, and cement content had the most significant impact on the fc′ of WFS‐containing concrete. Finally, a comparison was made between the proposed ACO + ANN model and four other AI models recently proposed in the literature, in which the performance criteria demonstrated that the proposed ACO + ANN model outperformed the models in the literature. [ABSTRACT FROM AUTHOR]

This study proposes an optimal approach for enhancing the quality of a weak electrical power distribution system by utilizing thyristor‐controlled series capacitors (TCSC). The proposed approach considers various system components, including shunt capacitors and distributed generators that rely on renewable energy sources, which are characterized by uncertainty due to changing weather conditions. The proposed system maximizes the benefits of these components by updating its parameters to improve the distribution power system's quality under all operating conditions, which change due to the varying energy generated by the distributed generators and load changes. The optimal TCSC values are calculated for all operating conditions using the Hyper‐Spherical Search with graded objective functions management strategy (HSSA‐GOFMS) algorithm. The GOFMS converts all restrictions into objective functions with limits and prioritizes the search for objectives according to a sequential system of priorities. This strategy prevents the possibility of non‐convergence. The results demonstrate the effectiveness of the proposed approach in improving the performance quality of a weak electrical distribution system. The proposed strategy achieves the target voltage level limits within nominal values, even with the interference of the system with generators distributed at variable power levels. Furthermore, the proposed approach enhances the performance quality of the system under a wide range of load changes. [ABSTRACT FROM AUTHOR]

Tapping weight is an important parameter in converter blowing process, wherein precisely predicting the quantity of steel required in an alloy baking converter can effectively guide the requirement of alloy ingredients. In practical production, the main approach is empirical estimation, despite its low accuracy. Employing a general neural network model for prediction requires to gather the converter blowing parameters and endpoint temperature measurement sampling parameters as the model inputs. However, this data cannot be obtained until the blowing process reaches its endpoint, rendering it impractical for alloy batching that requires advance preparation for baking. In this study, a principal component analysis–whale optimization algorithm–backpropagation algorithm (PCA–WOA–BP) neural network tapping prediction model is developed using raw material parameters available before alloy baking as input. This model is integrated into the intelligent alloy reduction model of a factory. The model achieves a regression coefficient R2 of 0.823, with 98.53% of furnaces having a prediction error of less than 2 t. The tapping weight of 20 consecutive heats in actual production is predicted; the error range is less than 80% within 1 t, and the converter tapping weight is predicted accurately. [ABSTRACT FROM AUTHOR]

To address the issue of diagnosing hard and soft faults in active power factor correction (APFC) power supply, this study analyzes failure modes resulting from aging and malfunction of various sensitive components. The power fault waveform patterns are initially analyzed based on the circuit's THD, current ripple value, and RMS value. The inductor current signals in different fault modes are then utilized to extract and construct time–frequency fusion fault features of the APFC power supply. Finally, these feature quantities are downscaled and optimized using the RF algorithm. The SOA‐KELM model of the APFC converter is proposed, and the feature vectors under different fault modes are used to classify and diagnose faults, achieving hard and soft fault detection of the converter. The experiments show that the method achieves 100% accuracy for hard fault diagnosis and 96.36% accuracy for soft fault diagnosis of the converter, demonstrating high diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Predicting carbon prices is crucial for the growth of China's carbon trading industry. This paper proposes a residual correction model that considers multiple influencing factors. First, the best historical data and main external factors input by the model are determined by using the partial autocorrelation function and Spearman correlation analysis, and the carbon price forecasting index system is constructed. Second, the whale optimization algorithm (WOA) is utilized to determine the optimal parameters of the extreme gradient boosting (XGBoost), and the WOA‐XGBoost model is built to perform preliminary carbon price forecasts and obtain the residual series. Finally, the carbon price residual series undergoes decomposition into multiple components utilizing the complete ensemble empirical mode decomposition for subsequent forecasting and the aggregation of outcomes. Experiments are conducted to predict two carbon trading markets in Hubei and Guangzhou, and a feature importance analysis is performed. The results indicate that the proposed hybrid model consistently outperforms the comparative models in terms of prediction accuracy. Furthermore, it is revealed that historical carbon prices and European Union carbon prices are the key factors influencing the prediction of carbon market prices. [ABSTRACT FROM AUTHOR]

A novel deep learning‐based optimization (DLBO) methodology is proposed for rapidly optimizing phononic crystal‐based metastructure topologies. DLBO eliminates the need for pre‐optimized data by leveraging the learned relation from metastructure features to bandgaps. It enables optimization based on qualitative/quantitative descriptions and forms a regular generalization domain to avoid misjudgments. DLBO achieves similar or better results to genetic algorithm (GA) and only requires 0.01% of the time GA costs. Metastructures with different periodic constants and filling fractions are also optimized, offering insights for balancing space, material, and vibration isolation. Based on a newly defined objective function, an economical metastructure is customized for subway‐induced vibrations; and its performance on vibration isolation is verified through a 3D finite element model. Additionally, the datasets and codes in this study are shared. [ABSTRACT FROM AUTHOR]

The research aims to improve the prediction of drug sensitivity on cancer cell lines using gene expression data and molecular fingerprints of drugs. The proposed study uses a deep learning model, BioMarkerX, trained on the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) datasets utilizing Particle Swarm Optimization technique to select specific genes as features. The model achieves high prediction accuracy with a Root Mean Square Error (RMSE) of 0.40 ± 0.02 and R2 of 0.83 ± 0.03 on the CCLE dataset, and an RMSE of 0.36 ± 0.05 and R2 of 0.83 ± 0.03 on the GDSC dataset. The approach also used an explainable artificial intelligence model to discover biological markers linked to cancer development. This can provide insights into targeted therapies for improving cancer treatment outcomes. Overall, the study presents an effective approach for identifying important biological markers relevant to cancer disease, aiding in the development of more efficient anticancer medications. [ABSTRACT FROM AUTHOR]

This study addresses the critical challenge of accurately classifying brain tumors using artificial intelligence. Early detection is crucial, as untreated tumors can be fatal. Despite advances in AI, accurately classifying tumors remains a challenging task. To address this challenge, we propose a novel optimization approach called PSCS combined with deep learning for brain tumor classification. PSCS optimizes the classification process by improving Particle Swarm Optimization (PSO) exploitation using Cuckoo search (CS) algorithm. Next, classified gene expression data of brain tumor using Deep Learning (DL) to identify different groups or classes related to a particular tumor along with the PSCS optimization technique. The proposed optimization technique with DL achieves much better classification accuracy than other existing DL and Machine learning models with the different evaluation matrices such as Recall, Precision, F1‐Score, and confusion matrix. This research contributes to AI‐driven brain tumor diagnosis and classification, offering a promising solution for improved patient outcomes. [ABSTRACT FROM AUTHOR]

Quality‐based design optimization is a process consisting of factor identification, finding an empirical/physical model between responses and factors, and finding a factor setting leading to the best quality levels. Response surface methodology is a well‐grounded data‐driven approach that applies a collection of statistical‐mathematical techniques to achieve the design requirements. Today's complex systems are usually affected by nuisance factors and have more than one output characteristic with some degree of correlation. This study aims to develop two‐stage stochastic programming for a multi‐response optimization model based on the degree of conformance as the most comprehensive metric for such design problems. The proposed approach can include the model imprecision in the optimization procedure using the probabilistic properties of the estimated parameters. This method would be applied in highly sensitive processes or costly operations that require a more accurate setting to avoid extra reworks or scraps. A simulated annealing algorithm with a boosted scenario checking modification has been applied to cope with the expensive computations for problem instances with a large sample size of uncertain parameters. The proposed model has also analyzed two cases from the literature, and the results support its superiority compared to the existing approaches. [ABSTRACT FROM AUTHOR]

The precise evaluation of predicting software development effort is a serious process in software engineering. The underestimates require more time, which seems to be the negotiation of complete efficient design and complete software testing. Thus, this paper devises a novel model for inspecting the effort taken for the design of software. The Mayfly Lion Optimization (MFLion) algorithm is devised, which ensembles the Mayfly Algorithm (MA) with the Lion Optimization Algorithm (LOA) for precise estimation. The deep maxout network (DMN) is adapted wherein the different weights are produced out of which the most suitable is infused considering the MFLion during model training. The optimal features are selected using recursive feature elimination (RFE) wherein the best features are selected and the remaining irrelevant features are eliminated from the list. The proposed MFLion obtained better performance with the smallest mean magnitude of relative error (MMRE) of 5.909 and the smallest root mean square error (RMSE) of 75.505, respectively. Each technique is produced using a separate database generated using the Promise software engineering repository. The outcomes produced from the assessment of the accuracies of models suggested that the MFLion‐DMN is a substitute to forecast software effort, which is extensively devised in engineering platforms. [ABSTRACT FROM AUTHOR]

In this article, a plasma‐metal model is studied as an electromagnetic absorber for stealth application. Epstain distribution is considered for plasma and the electromagnetic reflection of the structure is analyzed by the Green's function volume integral equation method. The attenuation of microwaves in plasma depends on microwave frequency, plasma electron density, spatial distribution of plasma, collision frequency, and plasma thickness. Using a metaheuristic optimization method, it is possible to change the parameters simultaneously in the specified domains to obtain conditions for which microwaves can be absorbed effectively in the specified frequency. In this paper, optimized plasma parameters related to ideal attenuation in desired frequency band are determined by this method. The advantage of this method is that the intervals related to the plasma parameters are selecting according to the practical conditions and in many cases prevent the production of high volume with high density plasma to achieve effective attenuation. The proposed method can be useful for designing adjustable devices in stealth applications. Proper management of power consumption in these devices is one of the results of adjustment. [ABSTRACT FROM AUTHOR]

Summary: Underwater IoT is incredibly helpful in monitoring a variety of jobs, from instrument monitoring to climate recording, from pollution management to natural catastrophe forecasting. Nevertheless, there exist various issues that have an impact on a network's efficiency such as the formation of void holes, excessive EC, and low PDR. As a result, the IGOR protocol is suggested in this study to increase PDR by reducing the percentage of void hole occurrence. The developed routing protocols' scalability is also examined. Here, the parameter optimization for the EC minimization and PDR maximization is performed by a meta‐heuristic optimization algorithm referred to as TSA. In order to verify that the suggested protocol is EC‐optimal by calculating the viable areas. In addition, suggested protocols are evaluated against contemporaries' benchmark routing protocols. The outcomes of the simulations clearly demonstrate that the suggested routing protocols obtained greater PDR than the current techniques. Additionally, there exists a reduction in the ratio of void hole incidence. Comparative research reveals that suggested routing protocols outperformed benchmark routing protocols by 80–81% in PDR. Further, the suggested routing procedures reduced the frequency of void holes by around 30%. [ABSTRACT FROM AUTHOR]

This article introduces a novel approach named HBA‐dHoSMO, which combines a continuous decentralized higher‐order sliding mode controller‐based observer (dHoSMO) with the honey badger algorithm (HBA), specifically designed for load frequency control in multi‐area power systems (MAPSs). Traditional sliding mode controllers (SMCs) employed in load frequency control of MAPSs often face challenges related to chattering and oscillations, leading to decreased robustness and stability. Additionally, tuning the parameters for these SMC designs to achieve optimal performance in MAPSs can be challenging. The HBA‐dHoSMO is proposed to address the issues of chattering and oscillations, while the optimal parameters for SMC design are obtained using HBA. The stability analysis of the entire system is conducted using linear matrix inequality and the Lyapunov stability theory, affirming the reliability and feasibility of the approach. A comprehensive set of case studies is performed under various configurations and conditions. Additionally, particle swarm optimization and tuna swarm optimization, in conjunction with SMC‐based and proportional–integral–derivative controllers, are examined for performance comparison. Simulation results demonstrate the superior performance of the proposed controller across all case studies. This is evidenced by the lowest integral time absolute error values recorded as 0.0133, 6.45 × 10−4, and 0.0167 for single‐, two‐, and three‐area power systems, respectively. [ABSTRACT FROM AUTHOR]

Metasurface‐based vectorial holography manifests itself as an advanced platform for large‐capacity information storage, holographic display, and cryptography. However, a convenient and effective reconfigurable vectorial hologram generation mechanism still remains a challenge. Here, a rotation‐driven reconfigurable vectorial holography scheme is developed via a dual‐layer hybrid metasurface device, in which radiation‐type and birefringent metasurfaces are cascaded hybridly. Thus, reconfigurable and highly customizable intensity and polarization response of holograms in the 3D space is achieved. Rotatable radiation‐type metasurface (RTM) serves as an incidence‐wavefront modulator to excite the non‐rotatable birefringent metasurface (BM). The gradient descent optimization inverse design method is introduced to achieve the high‐efficiency reconstruction of the Jones vector and Jones matrix distribution on both RTM and BM. On this basis, numerical analysis and experimental verification of 3‐D reconfigurable vectorial holography are demonstrated in the microwave region. This scheme implies a new paradigm for 3‐D reconfigurable vectorial holography and can lead to advances in high‐capacity optical display, switchable meta‐devices, and cryptography. [ABSTRACT FROM AUTHOR]

Summary: Microstrip patch antennas (MPA) are widely used in wireless communication systems due to their ease of manufacture, low cost, and low profile. This work proposes a proximity‐coupled rectangular microstrip patch antenna array (PRMPA) adaptable for 5G applications. However, the existing research works suffered from low gain, low efficiency, and narrow bandwidth. In addition, 5G communications suffer from path loss in high‐frequency bands. For managing this issue, the large gain antenna is essential. Hence, this research aims to design and fabricate high‐gain compact 2 × 2 slotted PRMPA for 5G applications. The antenna design is simulated on HFSS and operates at 28–37 GHz. Here, the antenna parameters like length, width, and feed line parameters are optimized using a metaheuristic optimization like the mayfly optimizer (MFO). Further, a circular‐shaped metamaterial split‐ring resonator (SRR) reduces the mutual coupling. Then, Rogers RT5880 is used as substrate material, and the design operates at 34.5 GHz. The gain and radiation efficiency achieved by the proposed antenna design are 15.4 dB and 94.4% for the simulated outcomes. Highlights: This research aims to introduce a compact 2 × 2 proximity‐coupled rectangular microstrip patch antenna (PRMPA) array for 5G communications at 28–37 GHz bands.This study's aim is to introduce a metaheuristic algorithm mayfly optimizer (MFO) to optimize the antenna's dimensions (length and width).This study provides the slots in a corner on both sides of the antenna to improve the gain and efficiency.This research aims to demonstrate the parameters of designed antennas, like single element and 2 × 2 array; they are compared based on gain, bandwidth, voltage standing wave ratio (VSWR), reflection coefficient, and radiation efficiency. [ABSTRACT FROM AUTHOR]

Summary: Unmanned aerial vehicle (UAV) path planning can be treated as a nondeterministic polynomial (NP) hard concern or an optimization problem. The conventional approaches are unable to effectively handle these issues due to discontinuity, non‐linearity, multi‐modality, and inseparability. On the other hand, meta‐heuristic algorithms are effective at tackling these issues because they are simple, adaptable, and derivation free. To enhance the performance in a variety of challenging circumstances, this paper proposes a novel Q‐learning‐based multi‐objective sheep flock optimizer with a Cauchy operator (Q‐MOSFO‐CA) to solve the constrained UAV path planning issues. The multi‐objective functions considered here are costs and constraints (threat, terrain, turning, climbing, and gliding constraints) to determine the feasible and optimal path. To avoid the probability of falling into the local optimum and to address the shortcoming of unbalanced convergence and also to maintain the exploitation and exploration capability, the Cauchy operator (CA) is integrated with the sheep flock optimization (SFO) algorithm. The Q‐learning model is introduced to balance both the global and local searches. Here, the exploration model performs the global search whereas the exploitation model performs the local search to attain an optimal solution. In the simulation scenario, the statistical analysis is conducted under two scenarios, and some essential measures such as the number of iterations at convergence (NIC), evaluation time (ET), energy consumption, and convergence analysis are determined. The proposed method obtains NIC of 1305 and 1436, ET of 12.8 and 15.2 s, and energy consumption of 20,600 and 21,465 J for both Scenarios 1 and 2, respectively. [ABSTRACT FROM AUTHOR]

This paper proposes a whale optimization algorithm (WOA)‐based partial transmit sequence (PTS) scheme called WOA‐PTS to reduce the high peak‐to‐average power ratio (PAPR) for universal filtered multi‐carrier (UFMC) systems. High PAPR is a prevalent challenge encountered in multi‐carrier systems. In the conventional PTS technique, the optimization of phase rotation factors is achieved through multiplication with the sub‐blocks. In this undertaking, WOA optimization is integrated as a phase optimizer in the PTS‐based PAPR reduction scheme. The experimental results show that, when compared with PTS‐based UFMC signal, the UFMC signal with WOA‐PTS can achieve 4.15 dB PAPR reduction at the complementary cumulative distribution function value of 10−3, additionally power spectral density performance and bit error rate also improved. [ABSTRACT FROM AUTHOR]

Ransomware is a serious security concern to mobile devices, as it prevents the use of the device and its contents until a ransom is paid, resulting in considerable financial losses for both people and corporations. The existing anti‐malware measures have shown to be inadequate in combatting new malware variants that utilize advanced evasion strategies like Polymorphic, Metamorphic, Dynamic Code Loading, Time‐based evasion, and Reflection. Furthermore, these primary defences have also suffered from low detection rates, significant false positives, high processing times, and excessive processing and power consumption that is inappropriate for smartphones. This paper offers the binary JAYA (BJAYA) for ransomware detection in Android mobile devices using the BJAYA optimization‐based algorithm. The developed algorithm's effectiveness has been assessed against two datasets, the 0–1 knapsack, and real ransomware dataset. The proposed BJAYA method surpassed the other algorithms on 85% of the 0–1 knapsack datasets. The suggested BJAYA method was also tested on a ransomware dataset in two phases. In the first stage of testing, BJAYA outperformed other standard classifiers with sensitivity and Gmean values of 97% and 98.2%, respectively. In the second stage of testing, BJAYA outperformed other GA, FPA, and PSO metaheuristic algorithms in terms of specificity, sensitivity, and Gmean. These findings indicate the applicability of the suggested BJAYA algorithm for ransomware detection. [ABSTRACT FROM AUTHOR]

In the upcoming epochs, conventional energy may deplete soon. Thus, the use of conventional energy in the power industries need to be supplemented by non‐conventional energy resources. This would result in loss of synchronisms in the power grids owing to the fact that solar and wind alternate their attributes expeditiously with change in atmospheric phenomenon. To ameliorate frequency deviation within a specific range automatic generation control (AGC) implements forced allowance on system operation. A three area thermal with photovoltaic (PV), electric vehicle (EV), wind system is considered under deregulated environment to develop and to judge the efficacy of newly developed cascade fractional order hybrid controller combination of (FOTID & 3DOF‐PID). Comparing the aforementioned controller to other controllers such as the three degree of freedom proportional‐integral‐derivative (3DOF‐PID), the fractional order tilt‐integral‐derivative (FOTID) and the proportional‐integral‐derivative (PID) justifies the system's effectiveness. This assessment has been accomplished by a trendy optimization technique such as hybrid whale optimization algorithm (HWOA). However, the main intent of this write‐up is to fabricate a cascade fractional order (CC‐FO) hybrid controller that would act as the new control mechanism for the proposed system under deregulated scenario. It has been found that the suggested CC‐FO hybrid controller stabilizes the system (i.e., Under step load disruptions, frequency deviation and tie‐line power become zero) in the shortest amount of time possible. Additionally, it is seen that the recommended controller can control a wide range of nominal loading circumstances and system characteristics, demonstrating its robustness. [ABSTRACT FROM AUTHOR]

The study proposes a modified version of African Vultures Optimization Algorithm (AVOA) to address the optimal power flow (OPF) issue. The developed optimizer is called Chaotic AVOA (CAVOA). Prior to applying CAVOA to the problem at hand, its accuracy is evaluated using 23 benchmark functions. The production powers of WTs that are interrupted are predicted using Weibull probability density functions (PDFs). Two additional costs, namely penalty cost and reserve cost, are incorporated into the goal function of the OPF. The simulation results of CAVOA are compared with those of the original AVOA to solve the OPF. The proposed OPF technique and its solution methodology are verified on the IEEE 30‐bus test system, taking into account the flexible alternating current transmission systems (FACTS) devices, which have several benefits such as reducing active power transmission loss, regulating power flow, and improving voltage stability/profile. The research's simulation results demonstrate that CAVOA is more effective in identifying the OPF's optimal solution by reducing the overall power cost and power losses. The results indicate that the CAVOA algorithm outperforms AVOA as a metaheuristic optimization algorithm due to its superior capability in addressing challenging OPF problems with a minimal convergence rate. For example, when comparing the two algorithms, CAVOA achieved a significant improvement. It successfully reduced the cost function by approximately 0.023% in Case I and 0.035% in Case II, while also decreasing power loss by 2.57% in Case II and 0.57% in Case I. Additionally, CAVOA exhibited a remarkable reduction of 5.36% in voltage deviation compared to AVOA in Case I. Furthermore, in Case III, CAVOA demonstrated a decrease of 0.64% in gross cost when compared to AVOA. [ABSTRACT FROM AUTHOR]

MRI is a popular imaging method for examining brain tumours. The ability to precisely segment tumours from MRI is absolutely essential for medical diagnostics and surgical planning. Manual tumour segmentation might be unrealistic for more comprehensive studies. Deep learning is the most widely used technique in medical diagnosis. For effective tumour dissection from brain MRI, this paper proposed a novel combination of FLAME and EHO Algorithm. FLAME is a type of clustering method that groups the most similar pixels in to a single cluster. EHO algorithm is one of the nature‐inspired metaheuristic optimization algorithms based on the social herding behaviour of elephants and swimming search methods. The proposed methodology's efficiency is validated through testing on various BraTS challenge datasets. The average computational time, mean squared error, peak signal to noise ratio, tanimoto coefficient, and dice score ‐ obtained are 23.3775 s, 0.213, 54.9669 dB, 54.6148%, and 84.053%, respectively. [ABSTRACT FROM AUTHOR]

This study presents an empirical modeling approach to reveal the effect of end milling parameters on the profile roughness parameters. Aluminium hybrid composite was melt‐stir die cast into the required work material, setting a predetermined weight fraction of reinforcements. The experimental design was adopted using the Taguchi meta‐heuristic algorithm for the selection of the levels of the end milling parameters. The profile roughness parameters namely arithmetic average roughness (Ra), root mean square roughness (Rq), and mean roughness depth (Rz) were considered as output responses, which were measured using stylus type surface roughness tester. Non‐linear second‐order empirical models were formulated and from them, the profile response parameters were predicted with 95 % accuracy. The results revealed that the depth of cut and spindle speed are contributing 54.22 % and 38.6 % respectively among the selected input parameters on the cumulative output. [ABSTRACT FROM AUTHOR]

Lung cancer is the most critical disease because it affects both men and women. Most of the time, lung cancer leads to death due to less health care and medical attention. In addition, lung cancer is difficult to identify in earlier stages due to the low‐level symptoms and risk factors. To overcome the complexity, effective techniques must predict lung cancer earlier. To attain the problem statement, an lung cancer identification system is developed with the help of a meta‐heuristic algorithm. The CT imageries obtained from the CIA database are analyzed step by step. The gathered image noise is removed by applying the mean filter, and the affected regions are segmented with the help of the Butterfly Optimization Algorithm‐based K‐Means Clustering (BOAKMC) algorithm. Afterward, various statistical features are derived, and the Supervised Jaya Optimized Rough Set related Feature Selection (SJORSFS) process is used to select the lung features. Finally, the lung cancer is identified using Autoencoder based Recurrent Neural Network (ARNN) classification algorithm, successfully recognizing the lung cancer features. Then the system's efficiency is evaluated using a MATLAB setup; here, 3000 are treated as training images and 2043 for testing images. The effective training enhances overall lung cancer prediction accuracy by up to 99.15%. [ABSTRACT FROM AUTHOR]

This study aims to develop a new artificial intelligence model that can produce explainable rules to predict the mix design and early‐age concrete compressive strength classes of recycled aggregate concrete (RAC). Unlike other black‐box machine learning methods and rule‐based algorithms, the study relies on a metaheuristic mechanism for explainability. This metaheuristic mechanism is not used for a traditional parameter optimization, but to automatically extract interpretable and interpretable rules from the experimental data. In the study, 30 series of RACs are produced, and the samples' 1‐ and 3‐day early‐age concrete compressive strength values are determined. Concretes produced using these strength values are classified. The labels defined for each concrete class are Class A (C 8/10), Class B (C 12/15), Class C (C 16/20), and Class D (C 20/25). The proposed intelligent classification model that consists of rule set automatically produces interpretable and comprehensible rules from data to determine the early‐age concrete compressive strength class and RAC mix amounts. In addition, the proposed method eliminates the black‐box disadvantages of classical machine learning methods with its explainability and interpretability feature. The sunflower optimization algorithm is adapted as the metaheuristic mechanism and a special fitness function and representative solution form are developed for automatic extraction of high‐quality comprehensible rules by simultaneously optimizing many different metrics. This paper is the first interpretable and comprehensible artificial intelligence model attempt used for early‐age compressive strength classification and mixture design of recycled aggregate concrete by balancing and optimizing both the accuracy and explainability. Proposed explainable intelligent classification model is tested against both well‐known state‐of‐the‐art machine learning algorithms and standard rule‐based methods on the produced real data. Promising results in terms of accuracy, precision, recall are obtained along with the explainability feature. [ABSTRACT FROM AUTHOR]

Summary: This article presents a study on the placement of multiple nulls as well as minimization of side lobe level in the radiation pattern using a planar hexagonal antenna array structure in two different vertical planes. The desired null depth is achieved to suppress the interference signal by the position‐only control of the uniformly excited isotropic antennas in the array structure. The immediate solution to the mentioned computational problem is reached by various meta‐heuristic optimization algorithms such as teaching learning‐based optimization (TLBO), symbiotic organism search (SOS), and moth fly optimization (MFO) within a considerably reduced processing time with a control over the design constrains. Various examples for diversified scenarios are demonstrated to place multiple deep nulls in the radiation pattern without compromising the pattern constraints and all other radiation pattern characteristics. This experiment sought to illustrate and quantify the unique benefits and limitations of proposed technique using three considered meta‐heuristic optimization algorithm. [ABSTRACT FROM AUTHOR]

Summary: In this manuscript, a novel energy detection‐based chaotic whale optimization algorithm (ED‐CWOA) is proposed to maximize the spectrum utilization, duration, and threshold in cognitive radio network (CRN). Here, the spectrum utilization is designed taking into account sensing duration and threshold in multi‐objective function is formulated by considering its primary user (PU) protection restrictions and secondary user (SU) throughput. To get the global optimal solution, the CWOA and the multi‐objective optimization problem are finally carried out on a single objective. The execution is performed on NS2 simulator. The simulation outcome demonstrates that the proposed ED‐CWOA system has lower delay 33.878%, 26.206%; higher delivery ratio 35.536%, 22.65%; lower Fairness Index 46.66%, 58.332%; lower energy consumption 25.462%, 35.524%; and higher throughput 96.82%, 49.28% for different nodes analyzed with the existing methods, like energy detection‐based bivariate Levy‐stable bat algorithm (ED‐BLSBA) and genetic algorithm‐based energy detection (ED‐GA), respectively. The simulation outcome indicates that the proposed system may be able to get the optimal global solutions efficiently and accurately. [ABSTRACT FROM AUTHOR]

This paper proposes a method based on scenario optimization and meta‐heuristic algorithm to optimize the power system stabilizers (PSSs) for enhancement of small‐signal stability (SSS) considering power system uncertainties such as photovoltaic generation, wind turbine generation, and load. The proposed method consists of two stages. In the first stage, the uncertainties are modeled using probability functions and scenarios are created based on them. These scenarios are then used to obtain a joint probability where the most likely probabilities are chosen for the second stage. The second stage utilizes these critical scenarios to formulate an optimization problem. The objective function of the optimization problem is to enhance system's small signal stability margin with PSS parameters as constraints. The proposed method is then solved using a new meta‐heuristic optimization algorithm known as Jelly Search Optimizer to obtain the optimized parameter of PSSs. The proposed method is tested on a two‐area and five‐area system, and the results show that the system SSS margin is improved for the majority of uncertainty situations. Moreover, the proposed method is around 1.6 times faster compared to the conventional probability method when tested in the modified two‐area system while 5.7 times faster when tested in the modified five‐area system. [ABSTRACT FROM AUTHOR]

Summary: The issue of underwater sensor network (UWSN) localization has led to the aim of techniques presented in recent years. In this paper, we develop Doppler shift with Archimedes Optimization Algorithm for localizing unknown nodes in UWSN. The projected method predicts that sink node plays a major function in managing the computational load contrasted with the remaining nodes in the network of underwater. This node localization is proceeding with frequency shifts of sound waves contrasted toward real, which are present once observer in addition source can be mobile as they do in a marine atmosphere. The proposed technique is utilized to compute the estimated position of an unknown sensor node; here Archimedes' optimization algorithm is utilized to reduce the error during localization of nodes in UWSNs. This proposed technique can be enhancing the accuracy of the localization of nodes in UWSNs. This proposed methodology can be implemented and evaluated with the help of performance metrics. To validate the proposed technique's efficiency, it is contrasted with conventional techniques like Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA). [ABSTRACT FROM AUTHOR]

Feature selection is a fundamental pre‐processing step in machine learning that aims to reduce the dimensionality of a dataset by selecting the most effective features from the original features. This process is regarded as a combinatorial optimization problem, and the grey wolf optimizer (GWO), a novel meta‐heuristic algorithm, has gained popularity in feature selection due to its fast convergence speed and easy implementation. In this paper, an improved binary GWO algorithm incorporating a novel Population Adaptation strategy called PA‐BGWO is proposed. The PA‐BGWO takes into account the characteristics of the feature selection problem and designs three strategies. The proposed strategy includes an adaptive individual update procedure to enhance the exploitation ability and accelerate convergence speed, a head wolf fine‐tuned mechanism to exert the impact on each independent feature of the objective function, and a filter‐based method ReliefF for calculating feature weights with dynamically adjusted mutation probabilities based on the ranking features to effectively escape from local optima. Experimental comparisons with several state‐of‐the‐art feature selection methods on 15 classification problems demonstrate that the proposed approach can select a small feature subset with higher classification accuracy in most cases. [ABSTRACT FROM AUTHOR]

From an engineering point of view, non‐linear systems are essential to the operation of control systems, because all systems actually have a non‐linear state in nature. In reality, there are many different kinds of non‐linear systems hidden by this negative definition. For successful analysis and control, the identification of non‐linear systems using unknown models is typically necessary. Till now, numerous approaches are developed for identifying non‐linear systems, but it cannot be employed with a large number of components. Moreover, system identification is typically restricted to output and input signals alone, also such systems are rarely used in reality. This is the primary justification for using non‐linear systems in this research. So, this research proposed a non‐linear model of system identification for large‐scale systems under the consideration of two systems: bilinear system and Volterra system. Therefore, a novel algorithm named Self Adaptive Penguin Search Optimization (SAPeSO) is introduced to attain the system characteristics properly and minimize the output variation. Finally, the effectiveness of the proposed work is compared with existing works in terms of various error measures. This research mainly focuses on the application‐oriented engineering problems. In particular, the Mean Absolute Error (MAE) of the proposed work for the Volterra system at 4000 samples is 18.83%, 14.05%, 8.88%, 29.72%, 19.91%, and 6.70% which is better than the existing bald eagle search (BES), arithmetic optimization algorithm (AOA), whale optimization algorithm (WOA), nonlinear autoregressive moving average with exogenous inputs‐ frequency response function + principal component analysis (NARMAX‐FRF+PCA), Global Gravitational Search Algorithm‐Assisted Kalman Filter (CGS‐KF), and sparse regression and separable least squares method (SR‐SLSM) methods, respectively. Finally, the error is minimum for the proposed model when compared with the other traditional approaches. [ABSTRACT FROM AUTHOR]

Summary: In the upcoming era, one of the emergent technologies is "Wireless Sensor Networks (WSNs)," which has provided various solutions in the field of defense, surveillance, and so on. But the main problems in the WSNs are security and energy‐efficient routing to the researchers. The deployment of security algorithms can monitor only the unauthorized signals to ensure security, but it lacks and leads to higher energy consumption, whereas the deployment of energy‐efficient routing algorithms is used only for the selection of routing paths, but it fails to ensure security. Hence, the main intent of this research work is to solve both problems by the following proposed models. Model 1 is "Identity‐Based Aggregate Signatures (IBAS)" that is the most effective security‐based framework protocol in this research work. Model 2 is the integration of IBAS and "Fuzzy Logic System (FLS)" that acts as an energy‐efficient algorithm, and here, FLS is used to find the optimal path from source to destination to transmit the data. Model 3 is the combination of IBAS and the "Whale Optimization Algorithm (WOA)" for higher energy efficiency and auto localization by analyzing the entire route in WSN. Model 4 is the combination of IBAS, WOA, and the "Reinforcement Theory of WOA (RTWOA)" energy‐efficiency model with a twofold and weight factor strategy for better speed and accuracy. These models ensure security, energy efficiency, and auto localization. The simulation parameters such as "Energy Efficiency," "Packet Delivery Ratio," "Average delay," "Throughput," and "Energy Consumption" are evaluated, and results show that the performance of energy efficiency and security is higher in Model 4 when compared to the other three models. [ABSTRACT FROM AUTHOR]

Visible light communication (VLC) is a promising technology that realizes short‐range wireless optical communication and illumination simultaneously. The use of light‐emitting diodes (LEDs) in lighting has accelerated the development of short‐range wireless optical technology. However, the limited switching frequency of LEDs used in lighting restricts the communication speed of VLC systems. Non‐orthogonal multiple access (NOMA) methods can be used to improve the sum rate in VLC systems. In this study, meta‐heuristic optimization algorithms are proposed to calculate the power allocation coefficients of users in indoor NOMA‐based VLC systems. During the calculations, many constraints used fairness and optical intensity, which were not taken into account in many studies in the literature. The proposed algorithms are compared with the previously proposed methods according to the sum rate results, considering the number of users increases and the user signal strength values differ. The obtained results revealed that metaheuristic methods demonstrate a significant improvement in terms of sum rate compared to other methods. In addition, the effects of parameters such as sum rate, fairness, and optical intensity on each other are investigated in the simulation environment. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Tolerance‐free assembly of modular concrete structures – optimization of module arrangement with metaheuristics Modular structures require dimensional stable components, since large process‐ or material‐related dimensional deviations in individual modules prevent assembly to the overall structure. The article presents a method for optimal placement based on metaheuristics. It helps to avoid cost‐intensive rework. The concept is applied to honeycomb structures in which the individual modules are swapped by permutation. With the so‐called Tabu Search and Simulated Annealing, two solving techniques are methodically used. For this purpose, the maximum nodal deviations of the assembled modules are determined with direct kinematics and compared with the admissible nominal hole clearance of bolted joints in order to assess the assembly capability of the modules. Tolerances from the shrinkage of the concrete on the one hand and those from the production steps of fabrication on the other hand are taken into account. Compared to a random arrangement of the modules, the reduction of maximum nodal deviations of 9 mm to the normalized nominal hole clearance of 3 mm succeeds with the help of the metaheuristics. Honeycomb structures with a width of approx. 7 m and a height of up to 45 m, which are particularly sensitive in terms of assembly, thus no longer require any reworking. Tabu Search proves to be the significantly more efficient solution algorithm, with six times less computation time and up to 24 % fewer deviations to be compensated. An example shows the practical application. [ABSTRACT FROM AUTHOR]

This paper presents a differential complementary metal oxide semiconductor inductor‐capacitor voltage controlled oscillator (LC‐VCO). The VCO is adopted from the gate‐to‐source capacitor feedback Colpitts VCO which has the advantage of large value of negative conductance. Due to the large negative conductance, the VCO has a more reliable startup oscillation at the lower currents. The main characteristics of the VCO such as negative conductance, oscillation frequency and phase noise are discussed and analysed. The value of parameters in this circuit have been determined using Non‐dominated Sorting Genetic Algorithm (NSGA‐II) to have the optimum performance. The designed VCO oscillates at 2.76 GHz under a supply voltage of 1.4 V. Power dissipation of the proposed VCO is 873 μW which is significantly lower than that of some other VCOs. The post‐layout simulation results of the proposed VCO are presented and compared with the performance of some other VCOs. [ABSTRACT FROM AUTHOR]

This article introduces a newly suggested meta‐heuristic arithmetic optimization algorithm (AOA) to reorganize the distribution network with the simultaneous distribution of distributed generators (DGs). The algorithm is designed to be flexible with a new approach that employs the scattering behavior of arithmetic operators in mathematics to discover the solution space more proficiently throughout the path of iterations. The usefulness of the AOA is demonstrated on 33‐bus, and 69‐bus radial distribution networks (RDNs) with network reconfiguration (NR) in the presence of DGs. The main objective of NR and effective DG arrangement in RDNs is to truncate the true power loss, reduce the voltage fluctuation, and voltage stability index enlargement. In order to increase the efficacy of the RDN, a multi‐objective function is created in view of active power loss, voltage deviation, and voltage stability index of the system with appropriate strategy without destroying the system restrictions. To illustrate the effectiveness of the RDN, four separate cases of NR and DG employment have been carried out. Four cases namely, base case (case‐1), reconfiguration (case‐2), simultaneous reconfiguration and DG installation with a unity power factor (case‐3), and simultaneous reconfiguration and DG installation with 0.95 lagging power factor (case‐4) are explored. The projected technique gives better results than other renowned methods, namely, fireworks algorithm (FWA), harmony search algorithm (HSA), refined genetic algorithm (RGA), and improved sine‐cosine algorithm (ISCA) strategy in view of 33, 69‐bus RDN at three dissimilar load conditions and its supremacy is confirmed. [ABSTRACT FROM AUTHOR]

Multiple uncertainties such as renewable energy output, energy purchase prices and integrated demand responses have brought about severe challenges to the safe and economic operation of integrated energy system (IES). To meet this challenge, this study takes the combined cooling heating and power as an example of IES and proposes an optimization model considering multiple uncertainties. First, the structure of IES is given, and these mathematical models and constraints are listed according to the energy supply characteristics. Second, considering uncertain factors such as the wind and solar output, comprehensive demand response and energy purchase price, the different characterization methods are selected to model the uncertainty sources by identifying the characteristics of multiple uncertainty sources. Then, combining the stochastic scenario method and robust optimization method, a day‐ahead optimal scheduling model of IES with multiple uncertainties is established, and the whale optimization algorithm is improved to obtain the optimal solution of the model. Finally, the actual data of an IES is selected to verify the rationality and effectiveness of the model. Meanwhile, the influence of uncertain factors on scheduling is introduced to verify the perfect coordination of economy and robustness of the model at runtime. [ABSTRACT FROM AUTHOR]

Summary: Internet of Things (IoT) is a network of smart things that connects people and objects for gathering and exchanging information by means of embedded sensor. IoT‐based wireless sensor network (WSN) nodes are monitored as a resource parameter at the variety of methods, including resource calculation, energy resource, and storage resource, with multipath routing protocols necessary to enable extended network lifespan and achieve high energy utilization. The existing routing protocols did not provide optimal path from the multipath route. Hence, in this manuscript, a Hybrid Marine Predators and Border Collie Optimization algorithm is proposed for multipath routing in IoTs. The main aim of this endeavor is to "increase network lifetime." IoT is simulated initially and multipath routing initiates in internet of things network. Multipath routing is achieved by proposed Hyb‐MP‐BCOA; this is the combination of Hybrid Marine Predators Algorithm and Border Collie Optimization algorithm. The proposed Hyb‐MP‐BCOA algorithm chooses the optimum path from the multiple path obtainable for routing, depending upon fitness parameter, like context awareness, network lifetime, residual energy, trust, and delay. Based on the fitness parameter, the optimal path is selected by using Hyb‐MP‐BCOA algorithm. Finally, the proposed Hyb‐MP‐BCOA‐MR‐IoT model is applied in MATLAB, and efficacy of proposed technique is estimated by using various performance metrics, like drop, normalized network energy, lifetime of network, delay, throughput, energy consumed, packet delivery ratio, and number of sensor nodes versus aggregation level. The proposed method attains lower delay of 14.285%, 50%, 53.846%, and 57.142%, lower packet drop of 14.285%, 50%, 53.846%, and 57.142%, lower energy consumption compared with existing methods such as multiobjective sun flower optimization with gray wolf optimizer (GWO) for multipath routing in IoT (SFO‐GWO‐MR‐IoT), multiobjective Whale Lion Fireworks optimization algorithm and fitness function for multipath routing in IoT (WLFA‐MR‐IoT), multipath routing in IoT using shuffled frog leaping algorithm (SFLA‐MR‐IoT), and multipath routing in IoT using hybrid Whale Optimization Algorithm‐Moth Flame Optimization (MFO)(hyb‐WOA‐MFO‐MR‐IoT). Finally, the safe communication is attained in the IoT network. [ABSTRACT FROM AUTHOR]

Summary: The heterogeneous ultra‐dense network (UDN) is a complex network environment where the small cells (SCs) are densely populated to acquire data transmission. The UDN is mainly adopted to deal with the explosive growth of mobile data and its consequential energy consumption issues. The UDN consists of mobile users, restricting the SCs from offering seamless services as the movement may disrupt the transmissions. To provide an effective solution, this paper introduces an energy‐efficient framework that enables effective data transmissions irrespective of the users' mobility. The proposed model considers the clustered SC deployment where the four‐tiered architecture is adopted. The architecture includes a macro base station (BS), microcells, picocells, and femtocells. The SCs are responsible for transferring the data received from the mobile users to the macro BS. The proposed model introduces the hybrid algorithm called the firefly oriented multiverse optimization (FF‐MVO) algorithm to identify the most optimal path for data transmission. This algorithm works iteratively to identify the optimal path to reach the macro BS for each transmission from the user. The proposed model is simulated in the network simulator 3 (NS3) platform, and the results are evaluated with the existing models. The outcomes proved that the proposed algorithm is more optimal than the other models in finding the optimal path to result in energy‐efficient transmissions. The proposed method achieved an average energy consumption of 0.24 J, an average energy efficiency of 10.965 bits/s/J and an average network throughput of 33.907 Gbps. [ABSTRACT FROM AUTHOR]