Multi-objective Grey Wolf Optimizer Research Articles

Advancing automatic text summarization: Unleashing enhanced binary multi-objective grey wolf optimization with mutation.

Automatic Text Summarization (ATS) is gaining popularity as there is a growing demand for a system capable of processing extensive textual content and delivering a concise, yet meaningful, relevant, and useful summary. Manual summarization is both expensive and time-consuming, making it impractical for humans to handle vast amounts of data. Consequently, the need for ATS systems has become evident. These systems encounter challenges such as ensuring comprehensive content coverage, determining the appropriate length of the summary, addressing redundancy, and maintaining coherence in the generated summary. Researchers are actively addressing these challenges by employing Natural Language Processing (NLP) techniques. While traditional methods exist for generating summaries, they often fall short of addressing multiple aspects simultaneously. To overcome this limitation, recent advancements have introduced multi-objective evolutionary algorithms for ATS. This study proposes an enhancement to the performance of ATS through the utilization of an improved version of the Binary Multi-Objective Grey Wolf Optimizer (BMOGWO), incorporating mutation. The performance of this enhanced algorithm is assessed by comparing it with state-of-the-art algorithms using the DUC2002 dataset. Experimental results demonstrate that the proposed algorithm significantly outperforms the compared approaches.

Optimization of off-grid hybrid renewable energy systems for cost-effective and reliable power supply in Gaita Selassie Ethiopia

This paper explores scenarios for powering rural areas in Gaita Selassie with renewable energy plants, aiming to reduce system costs by optimizing component numbers to meet energy demands. Various scenarios, such as combining solar photovoltaic (PV) with pumped hydro-energy storage (PHES), utilizing wind energy with PHES, and integrating a hybrid system of PV, wind, and PHES, have been evaluated based on diverse criteria, encompassing financial aspects and reliability. To achieve the results, meta-heuristics such as the Multiobjective Gray wolf optimization algorithm (MOGWO) and Multiobjective Grasshopper optimization algorithm (MOGOA) were applied using MATLAB software. Moreover, optimal component sizing has been investigated utilizing real-time assessment data and meteorological data from Gaita Sillasie, Ethiopia. Metaheuristic optimization techniques were employed to pinpoint the most favorable loss of power supply probability (LPSP) with the least cost of energy (COE) and total life cycle cost (TLCC) for the hybrid system, all while meeting operational requirements in various scenarios. The Multi-Objective Grey Wolf Optimization (MOGWO) technique outperformed the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) in optimizing the problem, as suggested by the results. Furthermore, based on MOGWO findings, the hybrid solar PV-Wind-PHES system demonstrated the lowest COE (0.126€/kWh) and TLCC (€6,897,300), along with optimal satisfaction of the village's energy demand and LPSP value. In the PV-Wind-PHSS scenario, the TLCC and COE are 38%, 18%, 2%, and 1.5% lower than those for the Wind-PHS and PV-PHSS scenarios at LPSP 0%, according to MOGWO results. Overall, this research contributes valuable insights into the design and implementation of sustainable energy solutions for remote communities, paving the way for enhanced energy access and environmental sustainability.

Multi-objective optimization for electric discharge drilling of waspaloy: A comparative analysis of NSGA-II, MOGA, MOGWO, and MOPSO

This study investigates the impact of pulse-on-time (TON), current (I), pulse-off-time (TOFF), voltage (V), and tool electrode speed (TES) on material removal rate (MRR), average surface roughness (Ra), power consumption (PC) and surface defects for electrical discharge drilling (EDD) of Waspaloy. Based on a Taguchi design with five factors and four levels, experimental trials reveal TOFF, TON, and TOFF as the most influential factors, contributing 50.45%, 34.29%, and 33.09% to MRR, Ra, and PC, respectively. To address conflicting conditions and optimize responses, non-dominated sorting genetic algorithm-II (NSGA-II), multi-objective genetic algorithm (MOGA), multi-objective grey wolf optimizer (MOGWO), and multi-objective particle swarm optimization (MOPSO) are employed. Comparative analysis of 8 optimal solutions provided by four optimization techniques, which exhibit less than 10% errors in confirmatory experiments, has been done, ensuring better prediction accuracy. MOPSO is preferable to the rest of the optimization techniques due to its lower percentage error. NSGA-II, MOGA, MOGWO, and MOPSO exhibit relative improvement in responses based on confirmatory experiments, establishing them as effective tools for exploring EDD applications. Higher current is confirmed as a primary contributor to increased surface damage through microscopic image analysis, and EDX analysis exposes carbon deposition migration on both the tool and workpiece.

Adaptive denoising for pipe leak vibroacoustic in multiple conditions using correlation coefficient matrix and multi-objective optimization

To achieve adaptive denoising of pipe leak vibroacoustic under low signal to noise ratio (SNR), a novel method based on correlation coefficient matrix was proposed for selecting the effective mode components obtained using variational mode decomposition (VMD). For the leak signals detected under different conditions, new indicators were designed to evaluate the denoising performance without using the prior knowledge of pure signals, and they were introduced as the objective function of multi-objective grey wolf optimizer (MOGWO). Subsequently, the best mode number K and penalty factor η of VMD were determined according to Pareto front, thus achieving adaptive denoising under multi-condition. To verify the proposed method, an experimental rig was built to simulate gas pipe leaks under multiple conditions with a SNR range from – 8 dB to 4 dB. The results showed that the proposed VMD-based denoising method achieved using the improved MOGWO can effectively suppress noise.

TBM tunneling strata automatic identification and working conditions decision support

It has become an important research topic to ensure the safe and efficient tunnel boring machine (TBM) tunneling in the construction of long distance and deep buried tunnels. This study aims to construct an assistant decision support system that integrates surrounding rock classification identification and tunneling parameters prediction optimization, providing quantitative decision guidance for driving TBM for main drivers. For this purpose, this study takes a TBM diversion tunnel project in Xinjiang as the research background, collects 8633 m length TBM on-site tunneling data, and constructs a TBM tunneling data sample database; The DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm was improved using kernel density estimation to achieve TBM tunnel surrounding rock classification based on tunneling performance and automatic identification. Compared with the traditional DBSCAN model, the Sk parameter average decreased by 30.0%, and the Ku parameter average increased by 181.9%; Adopting the opposition-based learning strategy and β-chaotic sequence method improved the MOGWO (multi-objective grey wolf optimization) algorithm to achieve optimal decision-making of TBM tunneling parameters under 12 working conditions. Compared to the traditional MOGWO model, the Pareto optimality solved by the improved MOGWO model increasing the improvement efficiency of the original value and sample mean by 14.82% and 11.46%, respectively. The on-site application results indicate that the assistant decision support system proposed in this study can provide construction guidance for TBM main drivers under different working conditions, improving the tunneling efficiency and safety of TBM.

Feature selection for sentiment analysis using hybrid multiobjective evolutionary algorithm

As the volume of data continues to grow, the significance of text classification is on the rise. This vast amount of data majorly exists in the form of texts. Effective data preparation is essential to extract sentiment data from this vast amount of text, as irrelevant and redundant information can impede valuable insights. Feature selection is an important step in the data preparation phase as it eliminates irrelevant and insignificant features from the huge features set. There exist a large body of work related to feature selection for image processing but limited research is done for text data. While some studies recognize the significance of feature selection in text classification, but there is still need for more efficient sentiment analysis models that optimize feature selection and reduce computational. This manuscript aims to bridge these gaps by introducing a hybrid multi-objective evolutionary algorithm as a feature selection mechanism, combining the power of multiple objectives and evolutionary processes. The approach combines two feature selection techniques within a binary classification model: a filter method, Information Gain (IG), and an evolutionary wrapper method, Binary Multi-Objective Grey Wolf Optimizer (BMOGWO). Experimental evaluations are conducted across six diverse datasets. It achieves a reduction of over 90 percent in feature size while improving accuracy by nearly nine percent. These results showcase the model’s efficiency in terms of computational time and its efficacy in terms of higher classification accuracy which improves sentiment analysis performance. This improvement can be beneficial for various applications, including recommendation systems, reviews analysis, and public opinion observation. However, it’s crucial to acknowledge certain limitations of this study. These encompass the need for broader classifier evaluation, and scalability considerations with larger datasets. These identified limitations serve as directions for future research and the enhancement of the proposed approach.

Multi-objective Grey Wolf Optimization based self configuring wireless sensor network

Multi-objective Grey Wolf Optimization based self configuring wireless sensor network

Research on the Optimal Design Approach of the Surface Texture for Journal Bearings

Aiming to improve the comprehensive performance of the journal bearing system, this paper presents a multi-objective adaptive scale texture optimization design approach. A mixed lubrication model for the textured journal bearing system is established by considering the effects of cavitation and roughness. The geometrical parameters of the textures were co-optimized using a multi-objective grey wolf optimizer to obtain the optimal texture schemes that are suitable for different operating conditions. Through this approach, the influences of different texture schemes under transient operating conditions can be investigated. According to the results, it was found that different texture schemes result in different friction reduction effects. Proper surface texture is beneficial in increasing the minimum oil film thickness and reducing the possibility of asperity contact. The adaptive scale texture exhibits strong adaptability and achieves significant hydrodynamic effects. Therefore, the developed approach provides valuable insights for the optimization design of journal bearing systems.

Multi-objective optimization of the performance for a marine methanol-diesel dual-fuel engine

Marine methanol-diesel compound combustion (DMCC) engines have received extensive attention in the shipping industry. This work aims to further improve the performance and evaluate the carbon reduction potential of the marine DMCC engine. First, the computational fluid dynamic model of the dual-fuel engine was built. Then, an intelligent regression method based on the grey wolf optimizer (GWO) algorithm optimized support vector machine regression (SVR) was proposed. The methanol substitution rate (MSR), excess air ratio (EAR), and diesel injection timing (DIT) were adopted as the decision variables to establish the regression models for NOx, CO, and CO2 emissions and indicated specific fuel consumption (ISFC). Subsequently, a multi-objective grey wolf optimizer (MOGWO) and entropy weight TOPSIS were employed to obtain the optimal case for achieving low NOx and CO emissions and ISFC. Finally, the maximum MSR was explored to achieve greater carbon reduction benefits while balancing the NOx emission and ISFC. The results showed that after GWO optimization, the coefficients of determination of the SVR regression models are all greater than 0.95, with mean square errors consistently below 0.015. It indicates that the regression models have excellent consistency and applicability. The optimal case calculated by MOGWO and entropy-weighted TOPSIS enables co-optimization of the NOx emission and ISFC while maintaining the CO emission below 600 ppm. Compared to the original case, the optimal case achieves reductions of 26.22 % for NOx and 13.39 % for ISFC. The exploration of the maximum MSR reveals that in the Pareto optimal set, the maximum MSR reaches 56.16 %, at which point the NOx and CO2 emissions reduce by 75.85 % and 59.32 % respectively, while the ISFC increases by 21.67 % compared to the original case.

Analysis and optimization of a sustainable hybrid solar-biomass polygeneration system for production of power, heating, drying, oxygen and ammonia

In this study, we introduce an innovative polygeneration system that combines solar and biomass energies to generate power, heating, hot air for the drying process, oxygen, and ammonia. The proposed polygeneration system comprises a solar tower subsystem (STS) integrated with two energy storage tanks (cold and hot), a gas turbine cycle (GTC), an organic Rankine cycle (ORC), a proton exchange membrane (PEM) electrolyzer, a thermoelectric generator (TEG), a cryogenic air separation unit (CASU), and an ammonia synthesis reactor (ASR). The GTC and the ORC equipped with the TEG are utilized for power production. The steam for the gasification process, hot air for the drying process and heating are produced by three regenerators. The PEM electrolyzer is used for the decomposition of water into hydrogen and oxygen, while the air separator (cryogenic method) is employed for the separation of air into nitrogen and oxygen. The separated oxygen is stored in an oxygen tank, while nitrogen and hydrogen are utilized for the ammonia generation. The system is evaluated from energy, exergy, economic and, environmental standpoints. Based on the findings, the proposed system is capable of generating approximately 163 MW of net power, 150 MW of heating, 100 MW of drying, 21.7 kmol/hr of oxygen and 23.24 kg/hr of ammonia. The total energy and exergy efficiencies, as well as the life cycle cost (LCC) and CO2 emissions are determined to be 70.68 %, 48.71 %, 1.175 × 109 $ and 381.3 kg/MWh, respectively. Additionally, a multi-objective Grey Wolf optimization algorithm is utilized to optimize the exergy efficiency, LCC, and CO2 emissions. The optimized results yield an exergy efficiency of 51.02 %, an LCC of 1.16 × 109 $, and a CO2 emissions of 359.73 kg/MWh. The proposed system demonstrates encouraging thermodynamic and economic performances, providing an appealing solution for efficient utilization of abundant solar and biomass resources.

Constrained multi-objective trans-media maneuver trajectory optimization for morphing unmanned aerial-underwater vehicles

In this study, the challenge of optimizing trans-media maneuver trajectories is addressed through the introduction of a Constrained Multi-Objective Grey Wolf Optimizer (CMOGWO). The objective function has been refined to assess solutions more accurately based on their feasibility and adherence to constraints. Three distinct Archives have been employed for categorizing and optimizing dominant, non-dominant, and feasible non-dominant solutions, each benefiting from specifically tailored optimization strategies. A hybrid search method, complemented by a watch-based strategy, is proposed for effectively guiding the population towards the Pareto front, ensuring the discovery of potential optimal solutions. Investigations have been conducted into the impact of initial altitude on trajectory optimization, with comparative simulations revealing the effectiveness of the proposed CMOGWO in managing unmanned aerial-underwater vehicle trajectories. The results demonstrate the potential of the CMOGWO in enhancing the reliability and feasibility of trajectory optimization in complex environmental conditions.

MBB-MOGWO: Modified Boltzmann-Based Multi-Objective Grey Wolf Optimizer.

The primary objective of multi-objective optimization techniques is to identify optimal solutions within the context of conflicting objective functions. While the multi-objective gray wolf optimization (MOGWO) algorithm has been widely adopted for its superior performance in solving multi-objective optimization problems, it tends to encounter challenges such as local optima and slow convergence in the later stages of optimization. To address these issues, we propose a Modified Boltzmann-Based MOGWO, referred to as MBB-MOGWO. The performance of the proposed algorithm is evaluated on multiple multi-objective test functions. Experimental results demonstrate that MBB-MOGWO exhibits rapid convergence and a reduced likelihood of being trapped in local optima. Furthermore, in the context of the Internet of Things (IoT), the quality of web service composition significantly impacts complexities related to sensor resource scheduling. To showcase the optimization capabilities of MBB-MOGWO in real-world scenarios, the algorithm is applied to address a Multi-Objective Problem (MOP) within the domain of web service composition, utilizing real data records from the QWS dataset. Comparative analyses with four representative algorithms reveal distinct advantages of our MBB-MOGWO-based method, particularly in terms of solution precision for web service composition. The solutions obtained through our method demonstrate higher fitness and improved service quality.

Performance analysis and multi-objective optimization of a novel CCHP system integrated energy storage in large seagoing vessel

To advance sustainable development and emission reduction efforts within the maritime industry, a novel combined cooling, heating, power, and storage (CCHPS) system is proposed. This system comprises a cogeneration system (ORC + ARC) and a thermal energy storage (TES) system. Initially, optimal working fluids are selected for the ORC and TES discharge systems. Subsequently, an analysis is conducted regarding the correlation between sensitivity parameters and system performance. Then, the cogeneration system's optimum design parameters are determined utilizing the multi-objective grey wolf optimization (MOGWO) algorithm. Finally, the TES system is analyzed and matched with the diesel generators to meet the ship's electricity demand. The CCHPS system achieves optimal thermal efficiency, payback period, annual CO2 emission reduction, and annual fuel savings of 30.07%, 6.22 years, 8910.49 tons, and 2791.74 kL, respectively. In contrast to the TES-absent system, the CCHPS system exhibits a 12.88% increase in thermal efficiency, along with a 25.48% improvement in annual fuel savings and annual CO2 emission reduction.

Optimization of Integrated Operation of Surface and Groundwater Resources using Multi-Objective Grey Wolf Optimizer (MOGWO) Algorithm

Optimization of Integrated Operation of Surface and Groundwater Resources using Multi-Objective Grey Wolf Optimizer (MOGWO) Algorithm

An Intelligent Charging Scheme for Lithium-Ion Batteries of Electric Vehicles Considering Internal Attenuation Modes

Charging control is one of the essential functions of battery management systems. Battery charging involves behavioral changes such as electricity, heat, and aging. Balancing these factors is crucial for the safe and efficient operation of batteries. This work proposes an intelligent charging scheme for lithium-ion batteries that considers charging time, temperature rise, and health losses. Firstly, charging aging experiments are conducted to investigate the effect of charging rate on battery aging. Specifically, half cell experiments are carried out to construct an electrode open circuit voltage model to explore battery aging modes. The aging mode attenuation model is proposed based on empirical formulas. Then, the charging multi-objective function is established based on the equivalent circuit, thermal, and aging empirical models. A multi-stage constant current charging optimization scheme has been developed based on the multi-objective grey wolf optimizer. Finally, the feasibility of three targets and dual targets fast charging schemes is verified through simulation and experiments.

An innovative process design and multi-criteria study/optimization of a biomass digestion-supercritical carbon dioxide scenario toward boosting a geothermal-driven cogeneration system for power and heat

In the pursuit of enhancing both sustainability and energy density in low-temperature, renewable energy-based cycles, the integration of high-temperature renewable streams is considered a key objective in multigenerational scenarios that focus on renewable energy. This integration is recognized for its ability to reduce irreversibility and facilitate the development of eco-friendly designs. Consequently, the development, analysis, and optimization of an innovative multigenerational system, which utilizes a combination of biomass feedstock and geothermal energy resources, are the aims of this study. In this system, the performance of a geothermal-driven subsystem is significantly enhanced by a biomass-fueled subsystem, contributing to a more efficient overall system. This enhancement involves the integration of biomass digestion with a supercritical CO2 process. The energetic flue gas generated in this process is then utilized to enhance the enthalpy level of geothermal water through a dual-flash process. This process includes an advanced Kalina cycle, enabling combined cooling, heating, and power generation. The feasibility of this structure is examined through a comprehensive analysis that encompasses thermodynamic and economic considerations. The performance optimization is targeted using the Multi-Objective Grey Wolf Optimization technique, and within this framework, two multi-criteria optimization scenarios are defined based on power and heat output, exergy efficiency, and the system's profitability. Furthermore, a detailed sensitivity analysis is conducted, where the impact of variations in five key decision parameters is evaluated. It is indicated by the results that 500.8 kW of power, 900.2 kW of heating, and 4.931 kW of cooling can be provided by the system, which also achieves an exergetic efficiency of 23.08 % and a payback period of 6.87 years.

A multi-objective grey-wolf optimization based approach for scheduling on cloud platforms

A cloud computing environment processes user workloads or tasks by exploiting its high performance computational, storage, of reducing and network resources. The virtual machines in the cloud environment are allocated to tasks with the aim of reducing overall execution time. The use of high performance resources incurs monetary costs, as well as high power consumption. The heuristic based approaches implemented for scheduling tasks are unable to cope with the complexity of optimizing multiple parameters. In this paper, we propose a multi-objective grey-wolf optimization based algorithm for scheduling tasks on cloud platforms. The proposed algorithm targets to minimize schedule length (overall execution time), energy consumption, and monetary cost required for executing tasks. For optimization, the algorithm incorporates steps that are performed iteratively for mimicking the behavior of grey wolves attacking their prey. It uses discrete values for positioning wolves for encircling and attacking the prey. The assignment of tasks to virtual machines is performed using the solution found after multi-objective optimization that incorporates weighted sorting for arranging solutions. Our experimentation performed using the CloudSim framework shows that the proposed algorithm outperforms other algorithms with performance improvement ranging from 3.98% to 16.07%, while considering the schedule length, monetary cost, and energy consumption.

Energy-latency trade-off analysis for scientific workflow in cloud environments: The role of processor utilization ratio and mean grey wolf optimizer

Cloud computing has demonstrated its effectiveness in handling complex data that requires substantial computational power, immediate responsiveness, and ample storage capacity. However, there are significant challenges when it comes to distributing intricate tasks, such as workflows structured in a Directed Acyclic Graph (DAG), across cloud resources. These challenges include increased energy consumption and delays experienced by end-users due to geographical disparities between users and service providers. To address these issues, this research work introduces an innovative solution that utilizes a multi-objective Grey Wolf Optimizer (GWO) algorithm for optimizing the allocation of complex workflows. The primary objective of this workflow optimization is to achieve the highest possible processor utilization ratio while adhering to latency constraints. This approach consists of a two-step process. In the initial step, it assesses processor utilization by calculating the number of completed transactions within a specified period divided by the average power consumption. This evaluation ensures that processors operate at the most power-efficient utilization levels, resulting in the highest service profit. In the second step, an optimal mapping algorithm assigns workflow tasks to the most efficient destinations. This is accomplished by applying the Mean Grey Wolf Optimization (GWO) algorithm, which is a metaheuristic technique. The goal is to find an optimal pairing of tasks and virtual machines (VMs) that balances multiple objectives. The simulation of this approach was conducted using the Java-based open-source CloudSim simulator version 5.0. The experimental results from a series of trials indicate that the ELSCiW approach leads to various improvements, including a reduction in latency ranging from approximately 5.35% to 12.92%, a decrease in end-to-end delay costs of about 5.03% to 13.80%, a reduction in energy consumption within the range of 4.71% to 11.19%, an enhancement in load balancing of around 14.84% to 37.30%, an improvement in performance degradation of about 12.52% to 35.14%, a decrease in overhead by approximately 17.27% to 26.92%, and an increase in the quality of service of roughly 56.21% to 64.85%. Furthermore, the ELSCiW approach outperforms recent state-of-the-art metaheuristic methods, as confirmed by statistical findings that emphasize the robustness of the proposed algorithm.

Coordinated optimization design of buildings and regional integrated energy systems based on load prediction in future climate conditions

The influence of global warming on building performance necessitates its consideration during the design phase. The conventional approach of separately designing the buildings and energy systems may not yield the globally optimal solution. To address this issue, this study proposes a coordinated optimization design approach for buildings and regional integrated energy systems (RIES) under future climate conditions. Firstly, a future regional load prediction model based on a Random Forest algorithm optimized by the Grey Wolf Optimizer is established. Next, a bi-level optimization model is proposed, with the upper level optimizing the building envelope to minimize costs and carbon emissions, and the lower level determining the optimal configuration capacity and operating strategy of RIES for cost minimization. Multi-objective grey wolf optimizer and CPLXE are employed to address the problem. Finally, the proposed method is validated using a case study of an office area in Jinan. The results show that the method can reduce costs by 14.6 % and carbon emissions by 50.93 % compared to the prototype building area with independent production systems, highlighting the potential of synergistic design between buildings and RIES. In addition, different optimization results were obtained under typical meteorological year, shared socioeconomic pathway (SSP) 245, and SSP585 climate scenarios, indicating the impact of future climate on building and RIES design. Therefore, it is necessary to consider climate change in future design work.

Optimization and inventory management under stochastic demand using metaheuristic algorithm.

This study considers multi-period inventory systems for optimizing profit and storage space under stochastic demand. A nonlinear programming model based on random demand is proposed to simulate the inventory operation. The effective inventory management system is realized using a multi-objective grey wolf optimization (MOGWO) method, reducing storage space while maximizing profit. Numerical outcomes are used to confirm the efficacy of the optimal solutions. The numerical analysis and tests for multi-objective inventory optimization are performed in the four practical scenarios. The inventory model's sensitivity analysis is performed to verify the optimal solutions further. Especially the proposed approach allows businesses to optimize profits while regulating the storage space required to operate in inventory management. The supply chain performance can be significantly enhanced using inventory management strategies and inventory management practices. Finally, the novel decision-making strategy can offer new insights into effectively managing digital supply chain networks against market volatility.