Search Results (79)

This research evaluates the performance of six metaheuristic algorithms in the active and reactive power management of wind turbines (WTs) integrated into an AC microgrid (MG). The population-based genetic algorithm (PGA) is proposed as the primary optimization strategy and is rigorously compared against five benchmark techniques: Monte Carlo (MC), particle swarm optimization (PSO), the JAYA algorithm, the generalized normal distribution optimizer (GNDO), and the multiverse optimizer (MVO). This study aims to minimize, through independent optimization scenarios, the operating costs, power losses, or CO₂ emissions of the microgrid during both grid-connected and islanded modes. To achieve this, a coordinated control strategy for distributed generators is proposed, offering flexible adaptation to economic, technical, or environmental priorities while accounting for the variability of power generation and demand. The proposed optimization model includes active and reactive power constraints for both conventional generators and WTs, along with technical and regulatory limits imposed on the MG, such as current thresholds and nodal voltage boundaries. To validate the proposed strategy, two scenarios are considered: one involving 33 nodes and another one featuring 69. These configurations allow evaluation of the aforementioned optimization strategies under different energy conditions while incorporating the power generation and demand variability corresponding to a specific region of Colombia. The analysis covers two-time horizons (a representative day of operation and a full week) in order to capture both short-term and weekly fluctuations. The variability is modeled via an artificial neural network to forecast renewable generation and demand. Each optimization method undergoes a statistical evaluation based on multiple independent executions, allowing for a comprehensive assessment of its effectiveness in terms of solution quality, average performance, repeatability, and computation time. The proposed methodology exhibits the best performance for the three objectives, with excellent repeatability and computational efficiency across varying microgrid sizes and energy behavior scenarios. Full article

This paper presents a novel energy management system for AC microgrids that integrates a parallel implementation of the Multi-Verse Optimizer (MVO) with the Successive Approximations method for power flow analysis. The proposed approach optimally schedules battery energy storage systems (BESSs) in both grid-connected and islanded modes, aiming to minimize energy losses and reduce ${\mathrm{CO}}_2$ emissions. Numerical evaluations on a 33-node AC microgrid demonstrate significant improvements: in the grid-dependent mode, energy losses drop from 2484.57 kWh (base case) to 2374.85 kWh, and emissions fall from 9.8874 Ton(CO₂) to 9.8693 Ton(CO₂). Under the self-powered configuration, energy losses and emissions are curtailed from 2484.57 kWh to 2373.53 kWh and from 16.0659 Ton(CO₂) to 16.0364 Ton(CO₂), respectively. The results highlight that the proposed method outperforms existing metaheuristics in solution quality and consistency. This work advances microgrid scheduling by ensuring technical feasibility, reducing carbon footprint, and maintaining voltage stability under diverse operational conditions. Full article

The accurate forecasting of financial flow data in power-grid operations is critical for improving operational efficiency. To tackle the challenges of low forecasting accuracy and high error rates caused by the long sequences, nonlinearity, and multi-scale and non-stationary characteristics of financial flow data, a forecasting model based on multi-verse expansion evolution (MVE²) and spatial–temporal fusion network (STFN) is proposed. Firstly, preprocess data for power-grid financial flow data based on the autoregressive integrated moving average (ARIMA) model. Secondly, establish a financial flow data forecasting framework using MVE²-STFN. Then, a feature extraction model is developed by integrating convolutional neural networks (CNN) for spatial feature extraction and bidirectional long short-term memory networks (BiLSTM) for temporal feature extraction. Next, a hybrid fine-tuning method based on MVE² is proposed, exploiting its global optimization capability and fast convergence speed to optimize the STFN parameters. Finally, the experimental results demonstrate that our approach significantly reduces forecasting errors. It reduces RMSE by 5.75% and 13.37%, MAPE by 22.28% and 41.76%, and increases R² by 1.25% and 6.04% compared to CNN-BiLSTM and BiLSTM models, respectively. These results confirm the model’s effectiveness in improving both accuracy and efficiency in financial flow data forecasting for power grids. Full article

Brent oil holds a significant position in the global energy market, as oil prices in many regions are indexed to it. Therefore, forecasting the future price of Brent oil is of great importance. In recent years, artificial intelligence techniques have been widely applied in modeling and prediction tasks. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS), a well-established AI approach, was employed for the time-series forecasting of Brent oil prices. To ensure effective learning and improve prediction accuracy, ANFIS was trained using nine different metaheuristic algorithms: Artificial Bee Colony (ABC), Selfish Herd Optimizer (SHO), Biogeography-Based Optimization (BBO), Multi-Verse Optimizer (MVO), Teaching–Learning-Based Optimization (TLBO), Cuckoo Search (CS), Moth Flame Optimization (MFO), Marine Predator Algorithm (MPA), and Flower Pollination Algorithm (FPA). Symmetric training procedures were applied across all algorithms to ensure fair and consistent evaluation. The analyses were conducted on the lowest and highest daily, weekly, and monthly Brent oil prices. Mean squared error (MSE) was used as the primary performance metric. The results showed that all algorithms achieved effective prediction performance. Among them, BBO and TLBO demonstrated superior accuracy and stability, particularly in handling the complexities of Brent oil forecasting. This study contributes to the literature by combining ANFIS and metaheuristics within a symmetric framework of experimentation and evaluation. Full article

This paper introduces an improved multi-objective grey wolf optimizer (IMOGWO) and demonstrates its application to the aerodynamic optimization of an axial cooling fan. Building upon the traditional multi-objective grey wolf optimizer (MOGWO), several improvement strategies were adopted to enhance its performance. Firstly, the IMOGWO started population initialization based on the Bloch coordinates of qubits to ensure a high-quality initial population. Additionally, it employed a nonlinear convergence factor to facilitate global exploration and integrated the inspiration of Manta Ray Foraging to enhance the information exchange between populations. Finally, associative learning was leveraged for archive updating, allowing for perturbative mutation of solutions in crowded regions of the archive to increase solution diversity and improve the algorithm’s search capability. The proposed IMOGWO was applied to five multi-objective benchmark functions, comprising three two-objective and two three-objective problems, and experimental results were compared with three well-known multi-objective algorithms: the non-dominated sorting genetic algorithm II (NSGA II), MOGWO, and the multi-objective multi-verse optimizer (MOMVO). It is demonstrated that the proposed algorithm had advantages in convergence accuracy and diversity of solutions, which were quantified by the performance metrics (generational distance (GD), inverted generational distance (IGD), Spacing (SP), and Hypervolume (HV)). Furthermore, a multi-objective optimization process coupled with the IMOGWO algorithm and Computational Fluid Dynamics (CFD) was proposed. By optimizing the design parameters of an axial cooling fan, a set of non-dominated solutions was obtained within limited iteration steps. Consequently, the IMOGWO also presented an effective and practical approach for addressing multi-objective optimization challenges with respect to engineering problems. Full article

The study introduces a novel standalone hybrid Energy Management System that combines solar PV, wind energy conversion systems, battery storage, and microturbines in order to provide reliable and efficient power under various operating conditions. The developed Quantum-Inspired Multiverse Optimization (QI-MVO) algorithm has thus far allowed for a remarkable efficiency of 99.9% and a 40% reduction in power losses when compared to conventional approaches. A rather speedy convergence to best solutions is exhibited by the methods, which take about 0.07 s for calculation, hence ensuring accurate optimization in complex energy systems. The QI-MVO-based EMS brings in improved reliability and optimal utilization of the system through balanced energy distribution and by maintaining system operational stability. In conclusion, the present work showcases QI-MVO as a sustainable and scalable energy management solution, which sets the stage for optimization strategies wherein hybrid energy management assumes a very important role. Full article

Green concrete uses incinerator ash or lightweight ash as a substitute for cement. It retains the properties of conventional concrete. Initial laboratory tests have determined the optimum mix design, weight variation, and compressive strength. Defined as an environmentally friendly material, green concrete reduces pollution or improves environmental conditions during production. This study incorporates incinerator ash, a toxic byproduct of waste disposal, into concrete production through a phased laboratory and numerical approach. A database for deep learning modeling was created using Convolutional Neural Networks (CNNs) and the Multi-Verse Optimizer (MVO) algorithm. After evaluating the efficiency and structure of the deep learning model through MATLAB coding, the focus shifted to analyzing the sensitivity of the input parameters on the output parameter using MATLAB for coding, training, and evaluation. The initial results indicate a significant effect of incinerator ash on the compressive strength of concrete. In addition, the deep learning modeling results show that the regression coefficient (R) of 90% reflects the accuracy and efficiency of the deep learning model for the current mix design. The error index, which is also reported, shows that the applied deep learning modeling method achieves optimal performance, with an average error of 0.14. The sensitivity analysis results of the introduced optimal model show that among the five input parameters, cement weight (W) has the greatest influence on compressive strength, as indicated by the statistical group distances from the baseline, percentage values, and average values. Full article

This study proposes a design procedure for the multi-objective aeroelastic optimization of a tow-steered composite wing structure that operates at transonic speed. The aerodynamic influence coefficient matrix is generated using the doublet lattice method, with the steady-state component further refined through high-fidelity computational fluid dynamics (CFD) analysis to enhance accuracy in transonic conditions. Finite element analysis (FEA) is used to perform structural analysis. A multi-objective transonic aeroelastic optimization problem is formulated for the tow-steered composite wing structure, where the objective functions are designed for mass and critical speed, and the design constraints include structural and aeroelastic limits. A comparative analysis of eight state-of-the-art algorithms is conducted to evaluate their performance in solving this problem. Among them, the Multi-Objective Multi-Verse Optimization (MOMVO) algorithm stands out, demonstrating superior performance and achieving the best results in the aeroelastic optimization task. Full article

One of the most important issues that can significantly affect the electric power network’s ability to operate sustainably is the optimal power flow (OPF) problem. It involves reaching the most efficient operating conditions for the electrical networks while maintaining reliability and systems constraints. Solving the OPF problem in transmission networks lowers three critical expenses: operation costs, transmission losses, and voltage drops. The OPF is characterized by the nonlinearity and nonconvexity behavior due to the power flow equations, which define the relationship between power generation, load demand, and network component physical constraints. The solution space for OPF is massive and multimodal, making optimization a challenging concern that calls for advanced mathematics and computational methods. This paper introduces an innovative metaheuristic algorithm, the Egyptian Stray Dog Optimization (ESDO), inspired by the behavior of Egyptian stray dogs and used for solving both single and multi-objective optimal power flow problems concerning the transmission networks. The proposed technique is compared with the particle swarm optimization (PSO), multi-verse optimization (MVO), grasshopper optimization (GOA), and Harris hawk optimization (HHO) and hippopotamus optimization (HO) algorithms through MATLAB simulations by applying them to the IEEE 30-bus system under various operational circumstances. The results obtained indicate that, in comparison to other used algorithms, the suggested technique gives a significantly enhanced performance in solving the OPF problem. Full article

With the rapid development of the metaverse, designing effective interfaces in virtual and augmented environments presents significant challenges. Additionally, keeping real-time sensory data flowing from users to their virtual avatars in a seamless and accurate manner is one of the biggest challenges in this domain. To this end, this article investigates a telepresence suit as an interface for interaction within the metaverse and its virtual avatars, aiming to address the complexities of signal generation, conversion, and transmission in real-time telepresence systems. We model a telepresence suit framework that systematically generates state data and transmits it to end-points, which can be either robotic avatars or virtual representations within a metaverse environment. Through a hand movement study, we successfully minimized the volume of transmitted information, reducing traffic by over 50%, which directly decreased channel load and packet delivery delay. For instance, as channel load decreases from 0.8 to 0.4, packet delivery delay is reduced by approximately half. This optimization not only enhances system responsiveness but also improves accuracy, particularly by reducing delays and errors in high-priority signal paths, enabling more precise and reliable telepresence interactions in metaverse settings. Full article

Background/Objectives: The accurate categorization of brain MRI images into tumor and non-tumor categories is essential for a prompt and effective diagnosis. This paper presents a novel methodology utilizing advanced Convolutional Neural Network (CNN) designs to tackle the complexity and unpredictability present in brain MRI data. Methods: The methodology commences with an extensive preparation phase that includes image resizing, grayscale conversion, Gaussian blurring, and the delineation of the brain region for preparing the MRI images for analysis. The Multi-verse Optimizer (MVO) is utilized to optimize data augmentation parameters and refine the configuration of trainable layers in VGG16 and ResNet50. The model’s generalization capabilities are significantly improved by the MVO’s ability to effectively balance computational cost and performance. Results: The amalgamation of VGG16 and ResNet50, further refined by the MVO, exhibits substantial enhancements in classification metrics. The MVO-optimized hybrid model demonstrates enhanced performance, exhibiting a well-calibrated balance between precision and recall, rendering it exceptionally trustworthy for medical diagnostic applications. Conclusions: The results highlight the effectiveness of MVO-optimized CNN models for classifying brain tumors in MRI data. Future investigations may examine the model’s applicability to multiclass issues and its validation in practical clinical environments. Full article

To address the noise issue in the measured vibration signals of spillway radial gate discharge, this paper utilizes the Multiverse Optimization Algorithm (MVO) to optimize the number of decomposition modes (K) and the penalty factor (α) in Variational Mode Decomposition (VMD). This approach ensures improved efficiency of VMD decomposition while maintaining accuracy. Subsequently, the obtained Intrinsic Mode Functions (IMFs) from VMD decomposition are classified based on Multi-scale Permutation Entropy (MPE). IMFs are divided into pure components and noisy components; the noisy components are processed with Wavelet Threshold Denoising (WTD), while the pure components are overlaid and reconstructed to obtain the denoised vibration signal of the gate. Comprehensive comparisons involving artificial signal simulations, gate flow-induced vibration model tests, and numerical simulations lead to the following conclusions: compared to other algorithms, the proposed combined denoising method (MVO-VMD-MPE-WTD) achieves the highest signal-to-noise ratio (SNR) in both the frequency and time domains for artificial signals, while yielding the lowest mean square error (MSE). In the gate flow-induced vibration model tests, the method significantly reduces noise in the vibration signals and effectively preserves characteristic information. The error in preserving characteristic information across model tests and numerical simulations is kept below 1%. Furthermore, compared to other optimization algorithms, the MVO demonstrates higher computational efficiency. The parameter-optimized combined denoising method proposed in this study provides insights into denoising measured vibration signals of hydraulic spillway radial gates and other drainage structures, and it opens possibilities for exploring more efficient optimization algorithms for achieving online monitoring in the future. Full article

The design of an effective antenna array is a major challenge encountered in most communication systems. A much-needed requirement is obtaining a directional and high-gain radiation pattern. This study deals with the design of a linear antenna array that radiates with reduced peak-side lobe levels (PSLL), decreases side-lobe average power with and without the first null beamwidth (FNBW) constraint, places deep nulls in the desired direction, and minimizes the close-in-side lobe levels (CSLL). The nature-inspired metaheuristic algorithm multi-verse optimization (MVO) is explored with other state-of-the-art algorithms to optimize the parameters of the antenna array. MVO is a global search method that is less prone to being stuck in the local optimal solution, providing a better alternative for beam-pattern synthesis. Eleven design examples have been demonstrated, which optimizes the amplitude and position of antenna array elements. The simulation results illustrate that MVO outperforms other algorithms in all the design examples and greatly enhances the radiation characteristics, thus promoting industrial innovation in antenna array design. In addition, the MVO algorithm’s performance was validated using the Wilcoxon non-parametric test. Full article

The surge in disordered EV charging demand, driven by the rapid growth in the ownership of electric vehicles (EVs), has highlighted the potential for significant disruptions in photovoltaic (PV)-connected distribution networks (DNs). This escalating demand not only presents challenges in meeting charging requirements to satisfy EV owners and grid fast-charging stations (GFCSs) but also jeopardizes the stable operation of the distribution network. To address these challenges, this study introduces a novel model called SOR&KANO for charging decisions, which focuses on addressing the dual-sided demand of GFCSs and EVs. The proposed model utilizes the salp swarm algorithm-convolutional neural network (SSA-CNN) to predict the PV output and employs Monte Carlo simulation to estimate the charging load of EVs, ensuring accurate PV output prediction and efficient EV distribution. To optimize charging decisions for reserved EVs (REVs) and non-reserved EVs (NREVs), this study applies the multi-verse optimizer (MVO) in conjunction with time-of-use (TOU) tariff guidance. By integrating the SOR&KANO model with the MVO algorithm, this approach enhances satisfaction levels for GFCSs by balancing the charging demand, increasing utilization rates, and improving voltage quality within the DN. Simultaneously, for EVs, the optimized scheduling strategy reduces charging time and costs while addressing concerns related to range anxiety and driver fatigue. The efficacy of the proposed approach is validated through a simulation on a modified IEEE-33 system, confirming the effectiveness of the optimal scheduling methods proposed in this study. Full article

The simulation analogy presented in this work enhances the accessibility of abstract quantum theories, specifically the stochastic hydrodynamic model (SQHM), by relating them to our daily experiences. The SQHM incorporates the influence of fluctuating gravitational background, a form of dark energy, into quantum equations. This model successfully addresses key aspects of objective-collapse theories, including resolving the ‘tails’ problem through the definition of quantum potential length of interaction in addition to the De Broglie length, beyond which coherent Schrödinger quantum behavior and wavefunction tails cannot be maintained. The SQHM emphasizes that an external environment is unnecessary, asserting that the quantum stochastic behavior leading to wavefunction collapse can be an inherent property of physics in a spacetime with fluctuating metrics. Embedded in relativistic quantum mechanics, the theory establishes a coherent link between the uncertainty principle and the constancy of light speed, aligning seamlessly with finite information transmission speed. Within quantum mechanics submitted to fluctuations, the SQHM derives the indeterminacy relation between energy and time, offering insights into measurement processes impossible within a finite time interval in a truly quantum global system. Experimental validation is found in confirming the Lindemann constant for solid lattice melting points and the ⁴He transition from fluid to superfluid states. The SQHM’s self-consistency lies in its ability to describe the dynamics of wavefunction decay (collapse) and the measure process. Additionally, the theory resolves the pre-existing reality problem by showing that large-scale systems naturally decay into decoherent states stable in time. Continuing, the paper demonstrates that the physical dynamics of SQHM can be analogized to a computer simulation employing optimization procedures for realization. This perspective elucidates the concept of time in contemporary reality and enriches our comprehension of free will. The overall framework introduces an irreversible process impacting the manifestation of macroscopic reality at the present time, asserting that the multiverse exists solely in future states, with the past comprising the formed universe after the current moment. Locally uncorrelated projective decays of wavefunction, at the present time, function as a reduction of the multiverse to a single universe. Macroscopic reality, characterized by a foam-like consistency where microscopic domains with quantum properties coexist, offers insights into how our consciousness perceives dynamic reality. It also sheds light on the spontaneous emergence of gravity in discrete quantum spacetime evolution, and the achievement of the classical general relativity limit in quantum loop gravity and causal dynamical triangulation. The simulation analogy highlights a strategy focused on minimizing information processing, facilitating the universal simulation in solving its predetermined problem. From within, reality becomes the manifestation of specific physical laws emerging from the inherent structure of the simulation devised to address its particular issue. In this context, the reality simulation appears to employ an optimization strategy, minimizing information loss and data management in line with the simulation’s intended purpose. Full article