Wolf Research Articles

Multiobjective flexible job-shop scheduling optimization for manufacturing servitization

PurposeWith the development trend of China’s service-oriented manufacturing moving toward intelligence and personalization, the deep integration of manufacturing and service has become a synergistic challenge for enterprises.Design/methodology/approachAn improved migratory bird optimization (IMBO) algorithm is proposed to solve the multiobjective FJSP model. First, this paper designs an integer encoding method based on job-machine. The algorithm adopts the greedy decoding method to obtain the optimal scheduling solution. Second, this paper combines three initialization rules to enhance the quality of the initial population. Third, three neighborhood search strategies are combined to improve the search capability and convergence of the solution space. Furthermore, the IMBO algorithm introduces the concepts of nondominated ranking and crowding degree to update the population better. Finally, the optimal solution is obtained after multiple iterations.FindingsThrough the simulation of 15 benchmark studies and a production example of a furniture enterprise, the IMBO algorithm is compared with three other algorithms: the improved particle swarm optimization algorithm, the global and local search with reinitialization-based genetic algorithm and the hybrid grey wolf optimization algorithm. The experiment results show the effectiveness of the IMBO algorithm in solving the multiobjective FJSP.Practical implicationsThe study does not consider the influence of disturbance factors, such as emergency interventions and equipment failures, on scheduling in actual production processing. It is necessary to further study the dynamic FJSP problem.Originality/valueThe study proposes an IMBO algorithm to solve the multiobjective FJSP problem. It also uses three initialization rules to broaden the range of the solution space. The study applies multiple crossover strategies to avoid the algorithm falling into local optimality.

NNBGWO-BRCA marker: Neural Network and binary grey wolf optimization based Breast cancer biomarker discovery framework using multi-omics dataset

Background and ObjectiveBreast cancer is a multifaceted condition characterized by diverse features and a substantial mortality rate, underscoring the imperative for timely detection and intervention. The utilization of multi-omics data has gained significant traction in recent years to identify biomarkers and classify subtypes in breast cancer. This kind of research idea from part to whole will also be an inevitable trend in future life science research. Deep learning can integrate and analyze multi-omics data to predict cancer subtypes, which can further drive targeted therapies. However, there are few articles leveraging the nature of deep learning for feature selection. Therefore, this paper proposes a Neural Network and Binary grey Wolf Optimization based BReast CAncer bioMarker (NNBGWO-BRCAMarker) discovery framework using multi-omics data to obtain a series of biomarkers for precise classification of breast cancer subtypes. MethodsNNBGWO-BRCAMarker consists of two phases: in the first phase, relevant genes are selected using the weights obtained from a trained feedforward neural network; in the second phase, the binary grey wolf optimization algorithm is leveraged to further screen the selected genes, resulting in a set of potential breast cancer biomarkers. ResultsThe SVM classifier with RBF kernel achieved a classification accuracy of 0.9242 ± 0.03 when trained using the 80 biomarkers identified by NNBGWO-BRCAMarker, as evidenced by the experimental results. We conducted a comprehensive gene set analysis, prognostic analysis, and druggability analysis, unveiling 25 druggable genes, 16 enriched pathways strongly linked to specific subtypes of breast cancer, and 8 genes linked to prognostic outcomes. ConclusionsThe proposed framework successfully identified 80 biomarkers from the multi-omics data, enabling accurate classification of breast cancer subtypes. This discovery may offer novel insights for clinicians to pursue in further studies.

Simulating the efficacy of wolf-dog hybridization management with individual-based modeling.

Introgressive hybridization between wolves and dogs is a conservation concern due to its potentially deleterious long-term evolutionary consequences. European legislation requires that wolf-dog hybridization be mitigated through effective management. We developed an individual-based model (IBM) to simulate the life cycle of gray wolves that incorporates aspects of wolf sociality that affect hybridization rates (e.g., the dissolution of packs after the death of one/both breeders) with the goal of informing decision-making on management of wolf-dog hybridization. We applied our model by projecting hybridization dynamics in a local wolf population under different mate choice and immigration scenarios and contrasted results of removal of admixed individuals with their sterilization and release. In several scenarios, lack of management led to complete admixture, whereas reactive management interventions effectively reduced admixture in wolf populations. Management effectiveness, however, strongly depended on mate choice and number and admixture level of individuals immigrating into the wolf population. The inclusion of anthropogenic mortality affecting parental and admixed individuals (e.g., poaching) increased the probability of pack dissolution and thus increased the probability of interbreeding with dogs or admixed individuals and boosted hybridization and introgression rates in all simulation scenarios. Recognizing the necessity of additional model refinements (appropriate parameterization, thorough sensitivity analyses, and robust model validation) to generate management recommendations applicable in real-world scenarios, we maintain confidence in our model's potential as a valuable conservation tool that can be applied to diverse situations and species facing similar threats.

UAV remote sensing monitoring of winter wheat tiller number based on vegetation pixel extraction and mixed-features selection

Tiller number serves as a crucial indicator of total yield in agriculture. Accurately monitoring tiller numbers is essential for guiding variable fertilization to optimize inputs and enhance final yields. Currently, most of the existing winter wheat tillering sensing monitoring methods cannot meet the three requirements of fast, accurate and efficient at the same time. In order to improve the monitoring accuracy and efficiency, this paper proposes a fast UAV remote sensing method for winter wheat tiller number estimation, combined with self-adaptive segmentation framework and multi-feature selection method. The self-adaptive segmentation framework is designed from the perspective of amplifying the difference of spectral information of ground objects. This framework fully considers the influence of complex environment, especially vegetation coverage, on parameter setting while using mathematical function means and polarization ideas. It aims to solve the problem of insufficient applicability of traditional segmentation methods under different environmental conditions. Subsequently, an optimized grey wolf search algorithm CMI-IGWO with multi-strategy fusion is proposed. Its novel evaluation criteria and iterative mechanism provide important algorithm support for the determination of the best inversion feature combination. The search algorithm combines statistics and information theory, comprehensively considers the linear and nonlinear relationships between variables, and guides the gray wolf to select the optimal features. Experiments show that the mixed features covering spectral, texture, color and shape information can make the final tiller number prediction model work best, achieving an R2 value exceeding 0.75. It affirms the efficacy and robustness of the proposed method in estimating winter wheat tiller numbers, providing valuable guidance for precise topdressing. This study provides a new feasible solution for predicting crop tiller number with high accuracy and efficiency using UAV remote sensing technology.

Enhancing wireless sensor network connectivity and coverage using Hybrid GWO‐HSA algorithm

SummaryWireless sensor networks (WSNs) are essential in environmental monitoring, healthcare, and industrial automation. Persistent connectivity and coverage challenges in WSN stem from intermittent node connectivity due to obstacles, signal interference, node failures, and compromised data reliability. Existing solutions, while useful, exhibit limitations in fully addressing these concerns. To confront these challenges, a proposed system introduces the Hybrid Grey Optimizer–Harmony Search Algorithm (Hybrid GWO‐HSA), merging adaptive routing protocols and efficient deployment techniques. The Hybrid GWO‐HSA system conducts an initial environmental analysis to pinpoint factors affecting node communication. It strategically deploys additional nodes to bridge coverage gaps, using the Grey Wolf Algorithm's capabilities to optimize node placement. Moreover, it employs the Harmony Search Algorithm to dynamically adjust communication paths based on real‐time network conditions, ensuring robust data transmission. The system workflow involves an environmental assessment followed by node deployment guided by the Grey Wolf Algorithm. Subsequently, the Harmony Search adapts communication paths to enhance connectivity. Simulations and practical experiments across diverse environments validate the Hybrid GWO‐HSA system's effectiveness. Results showcase substantial improvements: network lifetime of 13,200 s, a network delay of 37 ms, a coverage rate of 0.88, and an energy consumption of 590 J. This Hybrid GWO‐HSA‐based system establishes a resilient and efficient WSN infrastructure vital for reliable data collection and transmission in challenging settings. The Hybrid GWO‐HSA system offers a comprehensive approach to WSN connectivity and coverage issues by leveraging firefly and genetic algorithms, and it significantly enhances WSN performance and reliability across multifaceted application domains.

Estimation of Infrared Stellar Flux Based on Star Catalogs with I-GWO for Stellar Calibration

As on-orbit space cameras evolve toward larger apertures, wider fields of view, and deeper cryogenic environments, achieving absolute radiometric calibration using an all-optical path blackbody reference source in orbit becomes increasingly challenging. Consequently, stars have emerged as a novel in-orbit standard source. However, due to differences in camera bands, directly obtaining the stellar radiance flux corresponding to specific camera bands is not feasible. In order to address this challenge, we propose a method for estimating radiance flux based on the MSX star catalog, which integrates a dual-band thermometry method with an improved grey wolf optimization (I-GWO) algorithm. In an experiment, we analyzed 351 stars with temperatures ranging from 4000 to 7000 K. The results indicate that our method achieved a temperature estimation accuracy of less than 10% for 83.5% of the stars, with an average estimation error of 5.82%. Compared with previous methods based on star catalogs, our approach significantly enhanced the estimation accuracy by 75.4%, improved algorithm stability by 91.3%, and reduced the computation time to only 3% of that required by other methods. Moreover, the on-orbit star calibration error using our stellar radiance flux estimation method remained within 5%. This study effectively leveraged the extensive data available in star catalogs, providing substantial support for the development of an infrared star calibration network, which holds significant value for the in-orbit calibration of large-aperture cameras. Future research will explore the potential applicability of this method across different spectral bands.

Research on Photovoltaic Maximum Power Point Tracking Control Based on Improved Tuna Swarm Algorithm and Adaptive Perturbation Observation Method

In situations where photovoltaic (PV) systems are exposed to varying light intensities, the conventional maximum power point tracking (MPPT) control algorithm may become trapped in a local optimal state. In order to address this issue, a two-step MPPT control strategy is suggested utilizing an improved tuna swarm optimization (ITSO) algorithm along with an adaptive perturbation and observation (AP&O) technique. For the sake of enhancing population diversity, the ITSO algorithm is initialized by the SPM chaos mapping population. In addition, it also uses the parameters of the spiral feeding strategy of nonlinear processing and the Levy flight strategy adjustment of the weight coefficient to enhance global search ability. In the two-stage MPPT algorithm, the ITSO is applied first to track the vicinity of the global maximum power point (MPP), and then it switches to the AP&O method. The AP&O method’s exceptional local search capability enables the global MPP to be tracked with remarkable speed and precision. To confirm the effectiveness of the suggested algorithm, it is evaluated against fuzzy logic control (FLC), standard tuna swarm optimization (TSO), grey wolf optimization (GWO), particle swarm optimization (PSO), and AP&O. Finally, the proposed MPPT strategy is verified by the MATLAB R2022b and RT-LAB experimental platform. The findings indicate that the suggested method exhibits improved precision and velocity in tracking, efficiently following the global MPP under different shading conditions.

Optimal sizing of grid connected multi-microgrid system using grey wolf optimization

Renewable distributed energy resources (DERs) offer a promising and environmentally sustainable solution for providing energy. Nowadays, there has been significant attention in wind, solar Photovoltaic (PV), and hydrogen-based fuel cell (FC) systems due to their ability to provide cost-effective energy to replace conventional generations. However, the intermittent nature of renewable energy sources presents challenges and operational issues for fully renewable energy systems. To address this, integrating energy storage systems and effectively managing uncertainties related to both load and generation resources are crucial for mitigating such challenges. This paper proposes a hybrid grid-connected PV-wind-FC generation-based Multi-microgrid (MMG) system integrated with a Battery Energy Storage System (BESS) to meet the entire load demand of the adopted MMG-based IEEE 14-bus system. The aim is to ensure cost-effectiveness and enable energy trading with the main grid by optimizing system configurations. The study incorporates stochastic analysis to handle uncertainties related to load, meteorological data, and energy prices to optimize the configuration of DERs and BESS in the MMGs. A Grey Wolf Optimization (GWO) algorithm is employed to determine the optimal sizing of the proposed grid-connected MMG. The proposed algorithm has reduced the NPC from $431.796 million to $428.832 million and LCOE to 0.267$/kWh when load and generation data uncertainty and dynamic energy price has considered. The robustness of the proposed approach is evaluated by comparing results with those obtained using Particle Swarm Optimization (PSO) and JAYA algorithms. The GWO method demonstrates superior performance, resulting in lower total Net Present Cost (NPC), lower system capacity, and a lower Levelized Cost of Energy (LCOE) compared to its counterparts. Moreover, the GWO algorithm exhibits the fastest convergence, indicating its accuracy and robustness compared to PSO and JAYA algorithms.

An Explainable AI Model in Heart Disease Classification using Grey Wolf Optimization

Heart disease is one of the world’s leading causes of death. It is estimated that around one-third of all deaths are caused by heart disease in the entire world. Recently many research works have focused on using machine learning models to detect and warn patients about the occurrence of heart disease at the early stage. However, machine learning models provide promising results, and the performance of the classification is affected by various reasons which include imbalanced training, and missing values. There are three main contributions of this research work. Firstly, missing values are addressed by employing a grouping of instances. Secondly, a dual filter based feature selection is introduced to pick the most effective features and lastly, we make of Grey Wolf Optimization for optimizing the hyperparameters of the machine learning models. Together, these contributions aim to improve the robustness and efficiency of machine learning applications by addressing missing data, optimizing feature selection, and fine-tuning model parameters. The accuracy of 98.41% indicates the superiority of the proposed classification which is more than 17.15% than the existing machine learning models. On the other hand, we use Explainable AI (XAI) methods to make our proposed model interpretable.

Iron ore rock classification and mine remote sensing inversion based on spectroscopy and improved extreme learning machine

The classification of iron ore rock plays a crucial role in assisting the mine to develop scientifically sound mining strategies. Given the limitations of traditional manual identification and chemical techniques in terms of cost and detection time, it is crucial to develop an economical, high-precision, and efficient classification method for iron ore rocks. This paper takes an iron mine in Anshan as the research center and aims to establish an iron ore rock classification model by combining the improved extreme learning machine (ELM) with near-infrared spectra (NIRS). The NIRS of iron ore rock is denoised and feature extracted using the Savitzky–Golay filter and competitive adaptive reweighted sampling (CARS) methods, respectively. In this paper, we propose the IAdaBoost algorithm by introducing the sample similarity relationship into the weight-updating strategy of the AdaBoost integration algorithm. The weighted extreme learning machine (WELM) is adopted as the base learner for the IAdaBoost algorithm. Furthermore, we employ a multi-strategy improved gray wolf optimization (IGWO) algorithm to optimize the learning rate and the number of integrated base learners in the IAdaBoost algorithm. Consequently, we propose the IGWO-IAdaBoost-WELM model. Compared to other machine learning models, our proposed model performs better and can classify iron ore rocks consistently and efficiently. Meanwhile, we also used the model to invert the remote sensing images of the iron ore mining area, and the results show that the model can achieve fast, efficient, and large-scale iron ore rock classification, which provides an important reference for mining.

Optimal reconfiguration, renewable DGs, and energy storage units’ integration in distribution systems considering power generation uncertainty using hybrid GWO-SCA algorithms

ABSTRACT To optimize radial distribution systems, this study suggests the utilization of the Grey Wolf Optimizer (GWO), a hybrid metaheuristic optimization method, combined with the Sine Cosine method (SCA). The primary objective of this work is to enhance the distribution system by determining the most efficient network reconfiguration, sizing, and placement of various distributed energy sources in distribution system. The energy sources considered include capacitors, solar cells, wind turbines, biomass-based distributed generation units, and battery storage units. To achieve this goal, the proposed strategy incorporates the power loss sensitivity technique, which assists in identifying suitable candidate buses and accelerates the resolution process. Moreover, the model considers fluctuations in solar irradiance and wind speed using Weibull and Beta probability distribution functions, compensating for the intermittent nature of renewable energy sources and the variability in demand. To address power fluctuations, voltage surges, significant losses, and inadequate voltage stability challenges, battery energy storage, diesel generators, and dispatchable biomass DGs are employed to mitigate variability and enhance supply continuity. The proposed approach is evaluated and validated by comparing it to existing optimization strategies using IEEE 69-bus and 84-bus RDSs. The results demonstrate that the suggested technique achieves faster convergence to near-optimal solutions. The proposed methodology yields a significant reduction of up to 80% in power losses in the 69-bus system and a 35% reduction in the 84-bus system, signifying higher performance than existing methods.

Deep neural network accelerated-group African vulture optimization algorithm for unit commitment considering uncertain wind power

The unit commitment (UC) of power systems serves to plan out the starting and shutdown of units and the power generation of units for a future period of time. However, the UC problem for large-scale systems faces the problems of long solution time and insufficiently accurate solution. Uncertainty in wind power generation poses a great challenge in solving the unit combination problem. This work proposes a deep neural network accelerated-double layer optimization method (DNNA-DOM) to improve the solution efficiency, accuracy, and economy of UC. The outer layer of DNNA-DOM utilizes the proposed deep neural network accelerated-group African vulture optimization algorithm (DNNA-GAVOA) to optimize the on-off condition of conventional coal-fired power units. The inner layer of DNNA-DOM adopts quadratic programming to optimize the solution of the economic dispatch problem of load distribution. The GAVOA optimizes the simulated African vultures in four groups to obtain the optimal solution after diverse exploration, exploitation, and exploitation activities, with low complexity and high accuracy. This work first evaluates the performance of GAVOA by solving seven unimodal functions. Furthermore, the 10-unit simulation by incorporating wind power curves and related constraints is optimized through the DNNA-DOM. The results show that GAVOA outperforms the African vulture optimization algorithm (AVOA) and traditional metaheuristics like the particle swarm algorithm and gray wolf algorithm in terms of lower operating cost and optimization stability. The DNNA-DOM combined with DNNA-GAVOA in the outer layer results in an average daily cost reduction of $36.30 and a 45.1997 % speed increase compared to AVOA in the outer layer.

New host record of Thelazia callipaeda (Nematoda: Spirurida) with a notably wide host range and shared zoonotic lineage in Japan

Thelazia callipaeda (Nematoda: Spirurida: Thelaziidae) parasitizes the eyes of dogs, cats, humans, and various wild mammals, and is transmitted by drosophilid flies. In Japan, T. callipaeda is considered an emerging parasite that has expanded its endemic region northward. However, reports of its detection in mammals other than domestic animals and humans are scarce. This study reports the detection of T. callipaeda in Japanese red fox (Vulpes vulpes japonica), masked palm civet (Paguma larvata), Japanese badger (Meles anakuma), Japanese black bear (Ursus thibetanus japonicus), raccoon (Procyon lotor), Japanese raccoon dog (Nyctereutes viverrinus), domestic dog (Canis lupus familiaris), domestic cat (Felis silvestris catus), and human. Of these, the Japanese red fox, masked palm civet, Japanese badger, and Japanese black bear have been reported as novel host records. Sequence analysis of the cytochrome c oxidase subunit I gene of T. callipaeda revealed two unique lineages specific to Japan, with no regional or host species differences. These results suggest a wide host range for T. callipaeda, highlighting the significant role of wildlife as a reservoir for this parasite in Japan.

Hierarchical Gray Wolf Optimizer-Tuned Flexible Residual Neural Network With Parallel Attention Module for Bearing Fault Diagnosis

Hierarchical Gray Wolf Optimizer-Tuned Flexible Residual Neural Network With Parallel Attention Module for Bearing Fault Diagnosis

A short-term power load forecasting system based on data decomposition, deep learning and weighted linear error correction with feedback mechanism

Accurate power load forecasting enables Independent System Operators (ISOs) to precisely quantify the demand patterns of users and achieve efficient management of the smart grid. However, with the increasing variety of power consumption patterns, the power load data displays increasingly irregular characteristics, which posing great challenges for accurate load forecasting. In order to solve above problem, a novel power load forecasting system is proposed based on data denoising, customized deep learning and weighted linear error correction. Specifically, we first proposed an improved optimization algorithm IGWO-JAYA which enhanced the Grey Wolf Optimizer (GWO) algorithm by using Halton low-discrepancy sequence and the mechanism of JAYA algorithm. In data denoising, the proposed optimizer was employed to optimize the Variational Mode Decomposition (VMD), enabling data-driven intelligent denoising. The customized deep learning framework contained multi-layer Convolution Neural Network (CNN), Bi-directional Long Short-Term Memory (Bi-LSTM) and Multi-Head Attention mechanism. The effective integration of these layers can significantly improve the capacity for nonlinear fitting of deep learning. In weighted linear error correction, the IGWO-JAYA algorithm was employed to determine the appropriate weight for point forecasting values and residual forecasting values. By weighting them, the forecasting precision has been further enhanced. To verify the forecasting ability of the system, we conducted experiments on power load datasets from four states in Australia and found that it has the best performance compared with all rivals. In the discussion, we demonstrated the convergence efficiency of the IGWO-JAYA algorithm by CEC test function.

The bi-long short-term memory based on multiscale and mesoscale feature extraction for electric load forecasting

Accurate power load prediction is beneficial to the efficient use of electric energy and the orderly development of power systems. Given the strong volatility and complexity of power load series, a hybrid load forecasting method based on multiscale and mesoscale information fusion, signal decomposition, model optimization, and bi-long-short-term memory (BiLSTM) is proposed. Firstly, the load sequence is analyzed on different time scales, and the extracted multi-scale information and mesoscale information are fused to improve the perception ability. Secondly, the empirical wavelet transform (EWT) with adaptive decomposition ability is used to decompose the sequence and extract the rich feature information. Thirdly, the complexity, volatility, and uncertainty of each mode component were analyzed, the data features were fully mined, and the feature fusion was carried out by the TOPISIS evaluation method. The BiLSTM model and the GWO-BiLSTM model are used to predict the low-frequency component and the high-frequency component, respectively. The optimization of Grey Wolf optimization (GWO) algorithm can improve the BiLSTM model's ability to learn long-term time series. Finally, the analysis of application examples shows that compared with various prediction models, the prediction error of mixed model EWT-SGEO-BiLSTM is the smallest, MAPE is as low as 1.07 %, and goodness of fit R2 is 0.99 which verifies the accuracy and applicability of the intelligent model.

A hybrid swarm intelligence algorithm for region-based image fusion

This paper proposes a novel multi-hybrid algorithm named DHPN, using the best-known properties of dwarf mongoose algorithm (DMA), honey badger algorithm (HBA), prairie dog optimizer (PDO), cuckoo search (CS), grey wolf optimizer (GWO) and naked mole rat algorithm (NMRA). It follows an iterative division for extensive exploration and incorporates major parametric enhancements for improved exploitation operation. To counter the local optima problems, a stagnation phase using CS and GWO is added. Six new inertia weight operators have been analyzed to adapt algorithmic parameters, and the best combination of these parameters has been found. An analysis of the suitability of DHPN towards population variations and higher dimensions has been performed. For performance evaluation, the CEC 2005 and CEC 2019 benchmark data sets have been used. A comparison has been performed with differential evolution with active archive (JADE), self-adaptive DE (SaDE), success history based DE (SHADE), LSHADE-SPACMA, extended GWO (GWO-E), jDE100, and others. The DHPN algorithm is also used to solve the image fusion problem for four fusion quality metrics, namely, edge-based similarity index (QAB/F\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q^{AB/F}$$\\end{document}), sum of correlation difference (SCD), structural similarity index measure (SSIM), and artifact measure (NAB/F\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N^{AB/F}$$\\end{document}). The average QAB/F=0.765508\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q^{AB/F} = 0.765508$$\\end{document}, SCD=1.63185\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$SCD = 1.63185$$\\end{document}, SSIM=0.726317\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$SSIM = 0.726317$$\\end{document}, and NAB/F=0.006617\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$N^{AB/F} = 0.006617$$\\end{document} shows the best combination of results obtained by DHPN with respect to the existing algorithms such as DCH, CBF, GTF, JSR and others. Experimental and statistical Wilcoxon’s and Friedman’s tests show that the proposed DHPN algorithm performs significantly better in comparison to the other algorithms under test.

A novel modified Cheetah Optimizer for designing fractional-order PID-LFC placed in multi-interconnected system with renewable generation units

Establishing robust electrical interconnections between nations is a pivotal foundation for significant investments and addressing energy shortfalls in regions grappling with generational challenges. However, developing electrical load disruptions within interconnected systems can lead to substantial variations in frequencies and energy transmission. Load Frequency Control (LFC) is a crucial mechanism to mitigate these disruptions and ensure stable operations in interconnected regions. While meta-heuristics have been employed for LFC design, some techniques face challenges like early convergence and poor accuracy due to a lack of population diversity. In this study, a novel Modified Cheetah Optimizer (mCO) is proposed to optimize the parameters of LFC system, incorporating fractional-order proportional integral derivative (FOPID) controllers within multi-interconnected system with renewable energy integration. The mCO integrates learning-from-experience and random contraction strategies to enhance convergence accuracy and overcome local optima, demonstrating superior efficiency in solving optimization problems. The proposed mCO is evaluated by solving twelve functions from the CEC2022 test suite, showcasing its effectiveness. The optimization problem involves minimizing the Integral Time Absolute Error (ITAE) of the area control error, considering changes in frequencies and exchanged power, with controller parameters λd, kd, ki, kp, and μ to be identified. Two interconnected systems, photovoltaic (PV)-thermal and thermal-wind turbine (WT)-thermal-PV, are assessed under various load disturbances. The mCO is compared with other methods, including Modified Hunger Games Search Optimizer (MHGS), Driving Training-Based Optimizer (DTBO), Grey Wolf Optimizer (GWO), Aquila Optimal Search (AOS), and Cheetah Optimizer (CO). In the case of PV-thermal linked system, the proposed mCO succeeded in mitigating the ITAE by 19.21% compared to the reported MHGS and 8.63% compared to the conventional CO. In the four interconnected systems, the suggested approach reduced the ITAE by 89.21% and 15.26% compared to the reported MHGS and conventional CO, respectively. This confirmed the efficacy of FOPID-LFC, which was designed using the proposed mCO in all examined scenarios.

On-grid optimal MPPT for fine-tuned inverter based PV system using golf optimizer considering partial shading effect

Improving photovoltaic systems to better withstand variations in temperature and irradiance, lessen steady-state ripples, maintain high efficiency, require less tracking time, and have minimal complexity is becoming increasingly important. The PV array's overall power output is negatively impacted by partial shading because the shaded cells produce less power and consume less power overall, which lowers efficiency and creates localized hotspots. Although bypass diodes may be used to counteract these impacts by rerouting current around cells that are shaded, they can result in several peaks, which complicates maximum power point tracking (MPPT). To manage several peaks and optimize power output, metaheuristic algorithms are compulsory. A recently developed game-based optimization methodology known as Golf Optimization Approach (GOA) is used in this study to find a way to MPPT in a grid-connected photovoltaic (PV) system under partial shading conditions (PSCs). Additionally, the same suggested approach is applied to get optimal parameters for the inverter with the goal of optimizing its voltage and current regulators. It aims to evaluate a plan to lower the output voltage and current's total harmonic distortion and DC voltage error. The proposed technique is contrasted to other tracking approaches such as the traditional Perturb & Observe (P&O), Gray Wolf Optimization (GWO), and Sea-gull Optimization Approach (SOA). Simulation is performed in MATLAB R2022b/SIMULINK environment. The obtained outcomes demonstrate the ability of the suggested technique to enhance the utility grid's power quality and improve the tracking system under various partial shading effects and temperature alterations. The proposed technique achieves a tracking efficiency of 99.8 %, surpassing the effectiveness of both of the previous methods which are at 97.2 % and 96.7 %.

Many-objective optimization for overall performance of an electric sport utility vehicle under multiple temperature conditions based on natural gradient boosting model

In this study, significant efforts have been devoted to optimizing energy consumption and driving range of an electric sport utility vehicle (ESUV). Vehicle energy flow tests were conducted at different temperatures, and integrated simulation models were built and validated. Subsequently, Natural Gradient Boosting (NGBoost) model was constructed based on extensive datasets obtained by parallel simulation framework, and many-objective optimizations of ESUV design parameters were performed. The results show that energy consumption per 100 km under high and low temperatures increases by 19.3 % and 34.9 % compared to room temperature, while effective work of half-axis decreases by 12.9 % and 28 %, suggesting extreme environmental temperature significantly affects ESUV performance. Mean square errors (MSEs) of NGBoost model predictions are less than 0.12, indicating its reliable predictive capability for unknown data. Many-Objective Random Walk Grey Wolf Optimizer (MORW-GWO) algorithm achieves optimal or suboptimal results on 10 test problems, fully demonstrating its efficient convergence and excellent robustness. Battery recovered energy, effective work of half-axis and electricity consumption per 100 km of final optimization scheme are increased by 20.2 %, 9.0 % and 13.0 %, with the largest overall percentage improvement of 17.3 %. These findings can provide data support and directional guidance for optimization designs and performance improvements of ESUVs.