Grey Wolf Algorithm Research Articles

Evaluate Multi-Objective Optimization Model for Product Supply Chain Inventory Control Based on Grey Wolf Algorithm

Enterprise supply chain inventory control plays a critical role in modern enterprise management. Yet achieving multi-objective optimization remains challenging. This paper proposes a deep learning integrated model based on grey wolf optimization (GWO-DLIM) aimed at optimizing inventory holding costs, stockout costs, and order costs. The model integrates multi-layer perceptron (MLP), DeBERTa, and GWO, leveraging the powerful predictive and feature extraction capabilities of deep learning models, combined with the global optimization ability of the grey wolf algorithm, to achieve multi-objective optimization. These results demonstrate that the GWO-DLIM model significantly outperforms other comparative models across all metrics, exhibiting lower prediction errors and root mean square errors, as well as higher service levels. This study provides an effective multi-objective optimization solution for supply chain inventory management, holding significant theoretical and practical implications.

Design and Optimization of Fractional-Order FOPID Controller for Precise Power Head Control in Reverse Drilling Rigs Using Grey Wolf Algorithm

Design and Optimization of Fractional-Order FOPID Controller for Precise Power Head Control in Reverse Drilling Rigs Using Grey Wolf Algorithm

Improved Grey Wolf Algorithm: A Method for UAV Path Planning

Improved Grey Wolf Algorithm: A Method for UAV Path Planning

A Multi-Strategy Siberian Tiger Optimization Algorithm for Task Scheduling in Remote Sensing Data Batch Processing

With advancements in integrated space–air–ground global observation capabilities, the volume of remote sensing data is experiencing exponential growth. Traditional computing models can no longer meet the task processing demands brought about by the vast amounts of remote sensing data. As an important means of processing remote sensing data, distributed cluster computing’s task scheduling directly impacts the completion time and the efficiency of computing resource utilization. To enhance task processing efficiency and optimize the allocation of computing resources, this study proposes a Multi-Strategy Improved Siberian Tiger Optimization (MSSTO) algorithm based on the original Siberian Tiger Optimization (STO) algorithm. The MSSTO algorithm integrates the Tent chaotic map, the Lévy flight strategy, Cauchy mutation, and a learning strategy, showing significant advantages in convergence speed and global optimal solution search compared to the STO algorithm. By combining stochastic key encoding schemes and uniform allocation encoding schemes, taking the task scheduling of aerosol optical depth retrieval as a case study, the research results show that the MSSTO algorithm significantly shortens the completion time (21% shorter compared to the original STO algorithm and an average of 15% shorter compared to nine advanced algorithms, such as a particle swarm algorithm and a gray wolf algorithm). It demonstrates superior solution accuracy and convergence speed over various competing algorithms, achieving the optimal execution sequence and machine allocation scheme for task scheduling.

Optimization of a wideband discrete Raman amplifier in a P2O5-doped optical fiber using multi-objective grey wolf algorithm

This paper demonstrates the use of the multi-objective grey wolf algorithm to optimize a discrete Raman amplifier (DRA) in a P2O5-doped optical fiber. Specifically, the multi-objective grey wolf algorithm is combined with the DRA to carry out a process that seeks to maximize the gain and minimize the ripple. The P2O5-doped optical fiber employed in this study has a Raman gain coefficient spectrum with multiple peaks with different frequency shifts. This allows them to be combined in more complex ways than optical fibers with a single peak in the Raman gain spectrum. Consequently, the gain curve produced with this fiber has the potential to be more adjustable even when fewer pumps are used. Thus, this paper explores this fact to perform, to the best of our knowledge, the first specialized optimization process reported in the scientific literature of a wideband discrete Raman amplifier in a P2O5-doped optical fiber. With a different gain profile of this fiber compared to those of traditional standard optical fibers, it was possible to design a wideband DRA, going from 1530 nm to 1675 nm, covering C+L+U bands, maintaining a ripple of up to 8 dB with a net gain of 14 dB using only 3 pumps. Moreover, this work demonstrates for the first time, through a comparative analysis, that the multi-objective grey wolf algorithm performs better than the standard and well-known non-dominated sorting genetic optimization algorithm to optimize a DRA in a P2O5-doped optical fiber. The proposed DRA is a feasible, low-cost, and simple alternative for building fiber amplifiers for future high-bandwidth and wideband wavelength division multiplexing (WDM) communication systems, network infrastructures such as data centers, undersea cables, 5G and beyond, and cutting-edge research applications.

Multi-objective optimization of material extrusion additively manufactured parts from PETG feedstock material using grey wolf algorithm

Multi-objective optimization of material extrusion additively manufactured parts from PETG feedstock material using grey wolf algorithm

Tackle power outage effects for Egypt's energy crisis via localized optimum load shedding

This paper presents an approach for selecting optimal load-shedding values for individual subscribers or consumers. Implementing this method can bring positive outcomes for consumers by minimizing risks and economic losses caused by power outages. It enables network operators to address power supply deficits and overcome network issues, especially those affecting weaker areas. The optimal load-shedding values are determined by employing IGWA (Improved grey wolf algorithm) through a Graded Objective Function Strategy. The proposed method essentially centered on categorizing loads into two levels based on their importance. The first level includes essential loads, the disruption of which results in risks or significant economic losses. The second level comprises less critical loads, and they do not have more risks or economic losses when power outages. This approach applies specifically to residential loads. The control scheme diagram of disconnecting and reconnecting the current is established by estimating the critical load limit during power outage periods. The necessary arrangements can be made, leading to a proactive measure against risks and losses. The proposal has been implemented in Egypt's electricity network. this proposed method provides a simple and effective solution to the ongoing crisis and provides permanent resolution of problems arising from power outages.

Providing a framework to optimize the sustainable construction material supply chain with the possibility of horizontal transfers

The application of Supply Chain Management (SCM) principles has gained considerable attention across various industries to enhance operational efficiency and business performance. In the construction sector, SCM can guide managers in strategic planning and foster collaboration with suppliers, leading to more effective project execution. Despite its importance, limited research has focused on designing sustainable supply chains for construction materials under uncertain conditions. This study aims to bridge this gap by introducing a robust optimization framework that incorporates sustainable development criteria, specifically the minimization of harmful environmental effects and the reduction of worker unemployment. Additionally, the research introduces an innovative concept of horizontal transfers between downstream members, which not only reduces supply deficiencies but also indirectly increases profitability for these members. This article is a part of a Doctoral dissertation. It follows a two-step approach. First, suppliers of construction materials are ranked based on sustainable development criteria using a multi-criteria decision-making method. The best-ranked supplier is then integrated into a mathematical model for optimizing the supply chain design. Given the NP-hard nature of the problem, the CPLEX 12.1 solver with the epsilon constraint method is used to solve small-scale models. For larger-scale problems, three meta-heuristic algorithms are developed: multi-objective ant-lion, multi-objective gray wolf, and multi-objective dragonfly. The results show that while both the ant-lion and gray wolf algorithms deliver comparable average performance, the gray wolf algorithm excels in terms of solution time, making it more effective for real-time problem-solving. This makes the gray wolf algorithm the most efficient choice for addressing large-scale, multi-objective supply chain challenges. This research introduces a novel framework for designing sustainable construction supply chains under uncertain conditions. By focusing on criteria such as minimizing harmful environmental effects and reducing worker unemployment, this study addresses key aspects of sustainable development. Additionally, the innovative horizontal transfer mechanism and the insights into algorithmic performance provide valuable contributions to the field, especially for real-world applications in the construction industry.

Optimization of Synchronous Control Parameters Based on Improved Sinusoidal Gray Wolf Algorithm

High precision control is often accompanied by many control parameters, which are interrelated and difficult to adjust directly. It is difficult to convert the system control effect directly into mathematical expression, so it is difficult to optimize it by intelligent algorithm. To solve this problem, we propose an improved sinusoidal gray wolf optimization algorithm (ISGWO). In this algorithm, a particle crossing processing mechanism based on the symmetry idea is introduced to maximize the retention of the position information of the optimal individual and improve the search accuracy of the algorithm. In addition, a differential cross-perturbation strategy is adopted to help the algorithm jump out of the local optimal solution in time, which enhances the development capability of ISGWO. Meanwhile, the position update formula with improved sinusoidal can better balance the development and exploration of ISGWO. The ISGWO algorithm is compared with three improved Gray Wolf algorithms on the CEC2017 test set as well as the synchronization controller. The experimental results show that the ISGWO algorithm has better selectivity, speed and robustness.

Optimization and exergoeconomic analysis of a biomass-driven cogeneration system for power and cooling applications

Due to the increasing population and the expansion of industry in daily life, a significant depletion of fossil fuel resources is observed, accompanied by escalating environmental concerns. In response to these challenges, this study presents an optimized cogeneration system powered by renewable biomass, designed for simultaneous power and cooling generation. The system integrates a biomass gasification unit, a steam Rankine cycle, a helium-driven gas turbine cycle, and an ammonia-water-based two-stage absorption refrigeration cycle with a bleed line. Thermodynamic and exergoeconomic analyses reveal a net power output of 5356 kW, a cooling load of 360.9 kW, and an exergetic efficiency of 29.52 %. Financially, the system shows a net present value of 11.76 $M and a payback period of 6.51 years. A multi-objective optimization utilizing the gray wolf algorithm improves system performance through the employment of three diverse scenarios, which raises power output to 5864 kW, cooling load to 372 kW, and optimize net present to 14.28 $M with a reduced payback period of 5.96 years. The studied system is well-suited for industrial applications, rural electrification, and district energy systems, and offers a sustainable and economically viable energy solution.

Two-Layer Optimal Scheduling and Economic Analysis of Composite Energy Storage with Thermal Power Deep Regulation Considering Uncertainty of Source and Load

A two-layer scheduling method of energy storage that considers the uncertainty of both source and load is proposed to coordinate thermal power with composite energy storage to participate in the peak regulation of power systems. Firstly, considering the characteristics of thermal power deep peak regulation, a cost model of thermal power deep peak regulation is constructed and fuzzy parameters are used to manage the uncertainty of wind, photovoltaics, and load. Secondly, based on the peaking characteristics and operating costs of composite energy storage, a two-layer optimal scheduling model of energy storage is constructed. The upper layer takes pumped storage as the optimization goal to improve net load fluctuation and the optimal peak load benefit; the lower layer takes the system’s total peak load cost as the optimization goal and obtains a day-before scheduling plan for the energy storage system, using an improved gray wolf algorithm to process it. Finally, we verify the effectiveness of the proposed strategy based on an IEEE 39-node system.

Optimization of Operation Strategy of Multi-Islanding Microgrid Based on Double-Layer Objective

The shared energy storage device acts as an energy hub between multiple microgrids to better play the complementary characteristics of the microgrid power cycle. In this paper, the cooperative operation process of shared energy storage participating in multiple island microgrid systems is researched, and the two-stage research on multi-microgrid operation mode and shared energy storage optimization service cost is focused on. In the first stage, the output of each subject is determined with the goal of profit optimization and optimal energy storage capacity, and the modified grey wolf algorithm is used to solve the problem. In the second stage, the income distribution problem is transformed into a negotiation bargaining process. The island microgrid and the shared energy storage are the two sides of the game. Combined with the non-cooperative game theory, the alternating direction multiplier method is used to reduce the shared energy storage service cost. The simulation results show that shared energy storage can optimize the allocation of multi-party resources by flexibly adjusting the control mode, improving the efficiency of resource utilization while improving the consumption of renewable energy, meeting the power demand of all parties, and realizing the sharing of energy storage resources. Simulation results show that compared with the traditional PSO algorithm, the iterative times of the GWO algorithm proposed in this paper are reduced by 35.62%, and the calculation time is shortened by 34.34%. Compared with the common GWO algorithm, the number of iterations is reduced by 18.97%, and the calculation time is shortened by 22.31%.

A tunable dual-narrowband HTS filter using coding superconducting metamaterials aided by grey wolf algorithm

Abstract The design of tunable dual-narrowband high-temperature superconducting (HTS) filter based on coding superconducting metamaterials aided by grey wolf algorithm (GWA) is proposed in this paper. By virtue of the characteristics of right/left-handed bands and scale of deep sub-wavelength, the tunable metamaterials unit loaded with varactor is designed, with coupling types of coding pairs further studied. A tunable sixth-order HTS filter circuit is then constructed, whose layout is effectively optimized by newly developed GWA method. Simulated frequency responses demonstrate that the center frequencies of dual-band respectively locate at 170.1-177.2 MHz (Band I) and 185.2-194.1 MHz (Band II), with 3-dB fractional bandwidth (FBWs) of 0.65%-0.61% and 0.62%-0.57%. The deeply miniaturized circuit size is 0.037×0.046 λ2 g, and the close-to-band rejection level reaches 80-dB. Experimental results of fabricated prototype measured at 70 K validate the feasibility of described design method for high order tunable HTS filter.

Heat transfer modeling in a transformer using magneto-CFD analysis and GWO algorithm considering dynamic thermal model

One of the most expensive power system components is transformer. The correct use and health of the transformer increase the reliability of the power network.The transformer health is depend on careful planning for it. Therefore, hot spot temperature must be calculated for accurate transformer planning. Also, in order to increase the accuracy of transformer hot spot temperature estimation, the dynamic thermal model is used. On the other hand, the use of meta-heuristic algorithms has been less considered by researchers to calculate the transformer hot spot temperature. Thus, in this paper, the grey wolf, imperialist competitive and artificial bee colony algorithms are used to estimate the transformer hot spot temperature. Besides, electromagnetic analysis of the transformer is investigated using ANSYS-MAXWEL software. On the other hand, conjugate heat transfer modeling in the transformer is presented to compare the results with the grey wolf optimization algorithm. The results show that the grey wolf optimization algorithm can be a good alternative for transformer hot spot temperature estimation.

Integration of MPPT algorithms with spacecraft applications: Review, classification and future development outlook

The electric power system is one of the key subsystems of a spacecraft and is responsible for the acquisition, conversion, storage and control of energy during the spacecraft mission. Therefore, ensuring the adequacy and stability of energy sources has become an important part of the spacecraft design process. Currently, most spacecraft acquire energy through the solar cell arrays they carry, but the actual output power of the photovoltaic system is difficult to reach the peak power due to the influence of the on-orbit space environment such as alternating temperature changes and radiation. In order to improve the output efficiency of PV systems, researchers have proposed the maximum power point tracking method based on the operating characteristics of the PV cells, and the classical methods include the incremental conductance method, hill-climbing method, constant voltage method, and perturbation observation method. On its basis, in order to further improve the control accuracy and adaptability of MPPT under complex environment, related scholars have proposed several new MPPT algorithms, such as fuzzy algorithm and neural network algorithm based on the principle of intelligence, particle swarm optimization algorithm based on the principle of optimization, gray wolf algorithm and hybrid optimization algorithm based on multiple algorithms.This paper provides a comprehensive review of the topology, hardware architecture, mathematical models, and multiple DC-DC circuits of maximum power tracking PV systems; and reviews and summarizes the classical, intelligent, optimization, and hybrid types of maximum power point tracking methods, describing the principles, advantages, disadvantages, and applications of the different methods, respectively. Finally, an outlook on the future development and novel applications of maximum power point tracking methods for photovoltaic systems in aerospace is presented.

A Two-Stage Hybrid Stochastic–Robust Coordination of Combined Energy Management and Self-Healing in Smart Distribution Networks Incorporating Multiple Microgrids

This paper presents a two-stage hybrid stochastic–robust coordination of energy management and self-healing in smart distribution networks with multiple microgrids. A multi-agent systems approach is first used for coupling energy management and self-healing strategies of microgrids, based on expert system rules. The second stage problem, a framework similar to that of the first stage, is then established for the smart distribution networks. Then, hybrid stochastic–robust optimization is used to model the uncertainties of demand, energy price, power generation of renewable energy sources, demand of electric vehicles, and accessibility of zone agents. Further, the grey wolf algorithm is used to solve the formulated optimization problem and achieve an optimal and reliable solution. The proposal is validated on a 69-bus distribution network consisting of three microgrids. The results validate that the proposal minimizes microgrids’ utilization indices, such as energy costs, energy losses, and network voltage drops, while simultaneously managing a flexible distribution network. It is also verified that the proposed multi-agent system design provides a high-speed and optimized self-healing solution for the network.

Compact quad-channel filtering diplexer using meander hairpin ring resonator aided by grey wolf algorithm

Abstract In this paper, a compact quad-channel filtering diplexer using meander hairpin ring resonators (M-HRR) aided by grey wolf algorithm (GWA) is proposed. To realize a compact size of the multi-channel diplexer, the form of a meander-line structure is applied to the hairpin ring resonator for the miniaturization of the proposed quad-channel diplexer. Then, using the coupling topology of the 0° feeding structure, a transmission zero (TZ) is generated between two resonant frequencies of M-HRR to improve the band-edge selectivity and band-to-band isolation of the diplexer. To address the problematic challenge of the complexity in designing the multi-channel diplexer with a time-consuming optimization manually, an intelligent GWA is utilized to optimize the physical layout of the designed circuit to achieve the desired performance of the diplexer rapidly. Furthermore, different kinds of intelligent algorithms are implemented and compared under identical conditions. Finally, a quad-channel filtering diplexer centering at 3.28/3.75/4.32/4.75 GHz with 3-dB fractional bandwidth (FBW) of 5.4%, 2.9%, 4.9%, and 3.2% respectively is fabricated and measured. The circuit occupies a compact size of 0.22λg × 0.20λg. Good accordance is observed between the simulated and measured frequency responses.

An optimized model based on adaptive convolutional neural network and grey wolf algorithm for breast cancer diagnosis.

Medical image classification (IC) is a method for categorizing images according to the appropriate pathological stage. It is a crucial stage in computer-aided diagnosis (CAD) systems, which were created to help radiologists with reading and analyzing medical images as well as with the early detection of tumors and other disorders. The use of convolutional neural network (CNN) models in the medical industry has recently increased, and they achieve great results at IC, particularly in terms of high performance and robustness. The proposed method uses pre-trained models such as Dense Convolutional Network (DenseNet)-121 and Visual Geometry Group (VGG)-16 as feature extractor networks, bidirectional long short-term memory (BiLSTM) layers for temporal feature extraction, and the Support Vector Machine (SVM) and Random Forest (RF) algorithms to perform classification. For improved performance, the selected pre-trained CNN hyperparameters have been optimized using a modified grey wolf optimization method. The experimental analysis for the presented model on the Mammographic Image Analysis Society (MIAS) dataset shows that the VGG16 model is powerful for BC classification with overall accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) of 99.86%, 99.9%, 99.7%, 97.1%, and 1.0, respectively, on the MIAS dataset and 99.4%, 99.03%, 99.2%, 97.4%, and 1.0, respectively, on the INbreast dataset.

Multi-scale prediction of remaining useful life of lithium-ion batteries based on variational mode decomposition and integrated machine learning

Accurate and reliable prediction of the remaining useful life (RUL) of lithium-ion batteries (LIB) is very important for the safety of power systems. To solve the nonlinear and time-varying problems of LIB aging trajectories, an RUL prediction method based on variational mode decomposition (VMD) and integrated machine learning is proposed. First, the VMD algorithm is used to mine the internal information in LIB data to capture the long-term downward trend and capacity regeneration hidden in the capacity degradation. After that, the Elman neural network optimized by the gray wolf algorithm and Gaussian process regression were used to model and predict the intrinsic mode function (IMF) and residual sequence respectively. Next, the predicted IMF component and residual sequence are fused to obtain the LIB aging trajectory, and the capacity prediction error is corrected through the bidirectional long short-term memory network. Finally, the LIB data set is used as the test data of the algorithm. The results show that the proposed method has better robustness and nonlinear tracking ability, the mean absolute error is controlled within 1.5 %, and the RUL prediction error is within 3 cycles.

Multi-energy load forecasting for integrated energy system based on sequence decomposition fusion and factors correlation analysis

Considering the seasonal and cyclical fluctuation of loads and the complexity of multi-energy coupling, this paper proposes a novel load forecasting model based on sequence decomposition fusion and factors correlation analysis. Firstly, the variational mode decomposition (VMD) is used to decompose the highly complex load sequences and the novel influencing factors correlation analysis (ICA) is proposed to select strong factors and remove weak feature variables to construct the input and output set. Secondly, this paper proposes the combined forecasting framework MTL-CNN-BiGRU-Attention to simultaneously forecast the cooling, heat, and electricity loads, along with BiGRU used as the hard sharing layer to deeply explore the coupling information between different types of loads. Meanwhile, the gray wolf algorithm (GWO) is improved to accurately and quickly search for the optimal hyperparameters of the model. Finally, the dataset of a comprehensive energy station in Shanghai is used to test our model, and the results show that the MAPE of the cooling and electricity loads forecasting achieve 5.501% and 5.821% in summer and 5.921%, 7.899% and 7.541% for the cooling, heat and electricity loads in transition season and winter, which confirms the effectiveness and superiority of our model.