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An optimal robust fuzzy adaptive integral sliding mode controller based upon a multi-objective grey wolf optimization algorithm for a nonlinear uncertain chaotic system

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CHAOS SOLITONS & FRACTALS 2023年第1期167卷

作者： Mahmoodabadi, M. J. Sirjan Univ Technol Dept Mech Engn Sirjan Iran

This paper presents a novel robust fuzzy adaptive integral sliding mode controller designed via a multi-objective grey wolf optimization algorithm for a nonlinear uncertain chaotic system. To this end, proper integral surfaces are defined and a sliding mode stabilizer is designed to converge the system errors to zero. Next, the gradient descent approach is employed to tune the design gains of the integral sliding mode controller. Then, fuzzy rules are employed to regulate the coefficients of the proposed robust control approach. In order to acquire the proper parameters of the proposed controller and avoid trial-and-error processes, a multi-objective grey wolf optimization algorithm is employed to enhance the performance of the proposed controller. The challenging case study of a Duffing-Holmes oscillator, as a nonlinear autonomous uncertain chaotic system without stable equilibrium points, is considered to assess the behavior of the suggests optimal robust fuzzy adaptive integral sliding mode controller. The results of this study are compared with the outcomes of a distinguished work in the literature. Lastly, the discussions elucidate the efficiency of the proposed controller with respect to uncertain chaotic nonlinear systems in terms of optimal control inputs and minimum tracking error.

关键词： Robust control Integral sliding mode control multi-objective grey wolf optimization Fuzzy logic systems Uncertain chaotic systems Duffing-Holmes oscillator

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Efficient energy management and cost optimization using multi-objective grey wolf optimization for EV charging/discharging in microgrid

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 10卷

作者： Sharma, Swati Ali, Ikbal Department of Electrical Engineering Jamia Millia Islamia New Delhi 110025 India

The depletion of conventional energy sources coupled with the rising demand for electric vehicles (EVs) has significantly underscored the necessity for electric vehicle supply equipment (EVSE) with advanced energy supply management within microgrids. Effective energy management of EVs and EVSEs is imperative to satisfy EV owners and stabilize microgrids. This paper introduces multi-objective grey wolf optimization (MOGWO) to optimize EVSE and EV charging costs. MOGWO achieves substantial cost savings through dynamic pricing based on time of day, state-of-charge and hour-based scheduling which promotes off-peak charging thereby enhancing system efficiency and reducing costs. The algorithm also provides flexibility for trip interruptions and outperforms other optimization algorithms in minimizing EV charging/discharging costs by achieving reductions of up to ₹488 and obtaining average grid efficiency up to 76.41 % during charging and 87.41 % during discharging. Integrating Vehicle-to-Grid and Grid-to-Vehicle systems enhances microgrid resilience and delivers economic benefits to EV owners. The cost optimization for EV charging/discharging has been executed using MATLAB 2023a software to determine the most cost-effective charging paths by considering energy availability and grid conditions. The evolving sustainable energy landscape combined with MOGWO paves the way for smarter and more resilient microgrids in the era of electrified transportation. © 2024 The Author(s)

关键词： Electric vehicle Electric vehicle supply equipment Grid-to-vehicle Microgrid Microgrid centralized control system multi-objective grey wolf optimization Vehicle-to-grid

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Task offloading exploiting grey wolf optimization in collaborative edge computing

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JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS 2024年第1期13卷 23-23页

作者： Nujhat, Nawmi Shanta, Fahmida Haque Sarker, Sujan Roy, Palash Razzaque, Md. Abdur Mamun-Or-Rashid, Md. Hassan, Mohammad Mehedi Fortino, Giancarlo Univ Dhaka Dept Comp Sci & Engn Green Networking Res Grp Dhaka 1000 Bangladesh Univ Dhaka Dept Robot & Mechatron Engn Dhaka 1000 Bangladesh Green Univ Bangladesh Dept Comp Sci & Engn Dhaka 1461 Bangladesh King Saud Univ Coll Comp & Informat Sci Informat Syst Dept Riyadh Saudi Arabia Univ Calabria Dept Informat Modeling Elect & Syst I-87036 Arcavacata Di Rende Italy

The emergence of mobile edge computing (MEC) has brought cloud services to nearby edge servers facilitating penetration of real-time and resource-consuming applications from smart mobile devices at a high rate. The problem of task offloading from mobile devices to the edge servers has been addressed in the state-of-the-art works by introducing collaboration among the MEC servers. However, their contributions are either limited by minimization of service latency or cost reduction. In this paper, we address the problem by developing a multi-objective optimization framework that jointly optimizes the latency, energy consumption, and resource usage cost. The formulated problem is proven to be an NP-hard one. Thus, we develop an evolutionary meta-heuristic solution for the offloading problem, namely WOLVERINE, based on a Binary multi-objective grey wolf optimization algorithm that achieves a feasible solution within polynomial time having computational complexity of O(M-3), where M is an integer that determines the number of segments in each dimension of the objective space. Our experimental results depict that the developed WOLVERINE system achieves as high as 33.33%, 35%, and 40% performance improvements in terms of execution latency, energy, and resource cost, respectively compared to the state-of-the-art.

关键词： Collaborative mobile edge computing multi-objective grey wolf optimization Latency Service caching Task offloading

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Bi-objective grey wolf optimization algorithm combined Levy flight mechanism for the FMC green scheduling problem

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APPLIED SOFT COMPUTING 2021年 111卷 107717-107717页

作者： Zhou, Binghai Lei, Yuanrui Tongji Univ Sch Mech Engn Inst Ind Engn Shanghai 201804 Peoples R China

As more and more enterprises have laid great emphasis on eco-friendly manufacturing processes while enhancing agility, this paper focuses on the green scheduling problem of the Flexible Manufacturing Cell with material handling robots (FMC-R). In the FMC, each job is characterized by several operations, processing machines, and machining time. Robots at one straight track transfer jobs to the related machines. Distinguished from most of the FMC scheduling problems, this paper considers not only the operation sequencing of jobs but also the robots' transportation processes. A bi-objective mathematical model is established aiming to minimize the total makespan and the total energy consumption of the FMC system simultaneously. Due to the NP-hard nature of the problem, a Levy Flight and Weighted Distance-updated multi-objective grey wolf Algorithm (LWMOGWO) is proposed. It combines the Levy flight with multi-objective grey wolf Algorithm (MOGWO). A weighted distance updating mechanism is integrated to calculate the positions of individuals. A local neighbourhood search strategy is adopted, which makes it possible to take full advantage of the three leading wolves in population. The performance of the LWMOGWO algorithm is evaluated through groups of experiments by comparing with multiple hybrid meta-heuristic algorithms, hybrid particle swarm optimization (NGPSO) algorithm, an improved grey wolf optimization (IGWO) algorithm, MOGWO, NSGA-II (Non-dominated sorting genetic algorithm), and PLMEAPS. Results reveal the proposed LWMOGWO algorithm has a higher quality of solutions in solving the scheduling of the FMC-R problem. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Green scheduling Flexible manufacturing cells Material handling robots multi-objective grey wolf optimization Levy flight

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multi-criteria sensitivity study and optimization of a two-stage preheated SOFC system considering thermal characteristics

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APPLIED THERMAL ENGINEERING 2025年 261卷

作者： Xu, Kui Fan, Liyun Sun, Jinwei Huo, Haibo Jiang, Zejun Shen, Chongchong Wei, Yunpeng Harbin Engn Univ Coll Power & Energy Engn Harbin 150000 Peoples R China Shanghai Ocean Univ Coll Engn Sci & Technol Shanghai 201306 Peoples R China

At present, the pivotal challenges hindering the commercialization of the Solid Oxide Fuel Cell (SOFC) lie in system design and matching, thermal management safety, and performance optimization. To address these issues, a SOFC system-level model for the optimal control is proposed, integrating a two-stage preheater design, the electrochemical reaction mechanisms, and the influence of thermoelectric coupling effects on electrode temperature distributions. Furthermore, a comprehensive multi-criteria sensitivity analysis is conducted, incorporating thermodynamic, economic, and environmental indicators. The research demonstrates that there exist distinct mechanisms underlying the impact of varying trends in fuel utilization ratio and excess air ratio on the performance indicators of the SOFC system. Then, a detailed sensitivity analysis is undertaken, exploring the response trends of performance indicators under varying parameters. To validate the dynamic behavior of the objective functions, an intelligent learning approach based on the Artificial Neural Network is proposed. Additionally, a multi-objective grey wolf optimization process is introduced, aimed at enhancing the comprehensive performance of the SOFC system. The results of this study demonstrate that the proposed multi-objective optimization strategy achieves a reduction of 12.12% in maximum temperature gradient, 28.00% in Levelized Cost of Energy, and 4.76% in Mass Specific Emission at the optimal operating point. This underscores the effectiveness of the approach in balancing and optimizing the trade-offs between thermal stability, economic feasibility, and environmental impact of the SOFC system.

关键词： Solid Oxide Fuel Cell Artificial Neural Network multi-objective grey wolf optimization

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

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RENEWABLE ENERGY 2024年 224卷

作者： Ouyang, Tiancheng Tan, Xianlin Tuo, Xiaoyu Qin, Peijia Mo, Chunlan Guangxi Univ Sch Mech Engn Nanning Peoples R China Southeast Univ Sch Mech Engn Nanjing Peoples R China

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.

关键词： Waste heat recovery multi-objective grey wolf optimization Phase change heat storage Working fluid selection Power demand matching

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INTELLIGENT ROUTE PLANNING METHOD FOR UAV BASED ON SWARM INTELLIGENCE AND DEEP LEARNING TECHNOLOGY

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COMPUTING AND INFORMATICS 2024年第4期43卷 874-899页

作者： Yang, Jian Huang, Xuejun Natl Univ Def Technol Coll Elect Engn Hefei 230031 Anhui Peoples R China

Due to its potential applications in numerous industries, Unmanned Aerial Vehicles (UAVs) have gained considerable attention recently. UAV networks that are autonomous and decentralized have various practical uses, such as in disaster recovery, environmental monitoring, and security surveillance. Due to frequent route distractions and traffic congestion at high node speeds, the performance of routing systems in these networks drops considerably. UAVs with a mission to gather sensory data from various sources require meticulous route planning to decrease traffic congestion effectively. Due to flight time, range, and coverage area limitations, efficient route planning is crucial for maximizing the efficiency of UAV data collection. Optimal route planning and a delicate balancing act between these critical parameters are two of the biggest obstacles in sensory data gathering. This study presents a new method for dealing with these issues by developing an Intelligent Route Planning for Sensory Data Collection (IRP-SDC) system to optimize autonomous UAV route planning with congestion-aware modelling by considering time, distance, and area coverage limits. The IRP-SDC framework uses multi-objective grey wolf optimization and Deep Q-Learning (MOGWO-DQL) for smart UAV route planning. The MOGWO algorithm, developed after observing the hunting techniques of grey wolves, can perform a worldwide search, which helps determine the most efficient paths to take after gathering information. DQL, on the other hand, has adaptive learning capabilities that can modify the UAV's flight path in response to alterations in its external environment. The suggested framework combines the two techniques to maximize the usefulness of UAVs in gathering sensory data. Extensive trials were carried out to prove the efficacy of the proposed technique. The IRP-SDC system beats previous approaches concerning time, distance, and area coverage by providing an ideal route for a UAV to acquire sensory data

关键词： Autonomous UAVs intelligent route planning sensory data swarm intelligence Deep-Q-Learning multi-objective grey wolf optimization congestion-aware modeling

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A Novel Neutrosophic-based multi-objective grey wolf Optimizer for Ensuring the Security and Resilience of Sustainable Energy: A Case Study of Belgium

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SUSTAINABLE CITIES AND SOCIETY 2023年 96卷

作者： Ala, Ali Simic, Vladimir Pamucar, Dragan Jana, Chiranjibe Shanghai Jiao Tong Univ Dept Ind Engn & Management 800 Dongchuan Rd Shanghai 200240 Peoples R China Univ Belgrade Fac Transport & Traff Engn Vojvode Stepe 305 Belgrade 11010 Serbia Univ Belgrade Fac Org Sci Dept Operat Res & Stat Jove Ilica 154 Belgrade 11000 Serbia Yuan Ze Univ Coll Engn Taoyuan Taiwan Vidyasagar Univ Dept Appl Math Oceanol & Comp Programming Midnapore 721102 India

Today, the concept of energy security is of utmost importance. Also, sustainable, low-cost, emissions-conscious development is a significant challenge for energy resources. We formulate a bi-objective optimization framework to guarantee the long-term security and resilience of sustainable energy planning. Critical social factors such as balancing career development, fairness, and access rate to energy technologies are represented. Various renewable technologies are incorporated to produce high and resilient energy security. Diversity and decen-tralization concepts are taken into account, as well as the role of economy of scale. An advanced neutrosophic-based weighting approach is introduced to deal with the conflicting objectives of the formulated energy planning optimization model and transfer experts' views on the importance of energy security and energy costs. The multi -objective grey wolf optimization (MOGWO) and lexicographic weighted Tchebycheff (LWT) methods are applied to solve the problems of sustainable energy strategy. A case study of Belgium is provided to demonstrate the real -life applicability of the model. Neutrosophic-based MOGWO considerably outperforms LWT. The amount of utilization of solar and wind energy based on demand and efficiency will be achieved by 80% and 90%, respectively, until 2030. A strategic 15-year energy roadmap is presented for authorities to ensure security and resilience.

关键词： Energy security Sustainable energy strategy Social fairness multi-objective grey wolf optimization Type-2 neutrosophic number

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Smart Energy Management in Virtual Power Plant Paradigm With a New Improved multilevel optimization Based Approach

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IEEE ACCESS 2022年 10卷 50062-50077页

作者： Ali, Jannat Ul Ain Binte Wasif Kazmi, Syed Ali Abbas Altamimi, Abdullah Khan, Zafar A. Alrumayh, Omar Malik, M. Mahad Natl Univ Sci & Technol NUST US Pakistan Ctr Adv Studies Energy USPCAS E Islamabad Pakistan Majmaah Univ Coll Engn Dept Elect Engn Al Majmaah Saudi Arabia Mirpur Univ Sci & Technol Dept Elect Engn Mirpur Pakistan Univ Derby Inst Innovat Sustainable Engn Derby DE22 1GB England Qassim Univ Coll Engn Dept Elect Engn Unaizah 56453 Saudi Arabia

A virtual power plant (VPP) is a cloud based distributed power plant that aggregates the capacities of diverse distributed energy resources (DERs) for the purpose of enhancing power generation as well as trading or selling power on the electricity market. The main issue faced while working on VPPs is energy management. Smart energy management of a VPP is a complex problem due to the coordinated operation of diverse energy resources and their associated uncertainties. This research paper proposes a real-time (RT) smart energy management model for a VPP using a multi-objective, multi-level optimization-based approach. The VPP consists of a solar, wind and thermal power unit, along with an energy storage unit and some flexible demands. The term multi-level refers to three different energy levels depicted as three homes comprising of different amounts of loads. RT operation of a VPP is enabled by exploiting the bidirectional communication infrastructure. multi-objective RT smart energy management is implemented on a community-based dwelling system using three alternative algorithms i.e., hybrid optimal stopping rule (H-OSR), hybrid particle swarm optimization (H-PSO) and advanced multi-objective grey wolf optimization (AMO-GWO). The proposed technique focuses on achieving the objectives of optimal load scheduling, real-time pricing, efficient energy consumption, emission reduction, cost minimization and maximization of customer comfort altogether. A comparative analysis is performed among the three algorithms in which the calculated real-time prices are compared with each other. It is observed that on average H-PSO performs 7.86 % better than H-OSR whereas AMO-GWO performs 10.49% better than H-OSR and 5.7% better than H-P-SO. This paper concludes that AMO-GWO is the briskest, most economical, and efficient optimization algorithm for RT smart energy management of a VPP.

关键词： Energy management optimization Stochastic processes Mathematical models Real-time systems Uncertainty Programming Hybrid optimal stopping rule hybrid particle swarm optimization JADE multi-agent system multi-objective grey wolf optimization smart energy management

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greywolfLSM: an accurate oil spill detection method based on level set method from synthetic aperture radar imagery

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EUROPEAN JOURNAL OF REMOTE SENSING 2022年第1期55卷 181-198页

作者： Aghaei, Nastaran Akbarizadeh, Gholamreza Kosarian, Abdolnabi Shahid Chamran Univ Ahvaz Fac Engn Dept Elect Engn Ahvaz Iran

Oil spill detection (OSD) in marine areas is an application of synthetic aperture radar (SAR) images to protect aquatic life. In this paper, a new oil spill detection algorithm based on level set method (LSM) is presented. Dark spot detection, feature extraction, and classification are the main steps in the proposed method. In the first step, a new dark spot detection method in SAR images is presented, which introduces a combination of multi-objective grey wolf optimization (MOGWO) and K-means clustering to find the best threshold level for image segmentation. This method overcomes the K-means clustering by choosing optimal number of clusters and centers. In the feature extraction step, a vector consisting of 45 arrays of calculated Legendre moments in the main, gradient and radon transform of the image is used. The oil-suspicious areas are also discriminated by the supported vector machine (SVM) classifier, and their boundaries are applied as an initial contour to a new hierarchical region-based level-set method (HRLSM). Experiments are performed on the images acquired by Envisat, UAVSAR, TerraSAR-X, and Sentinel1 satellites. The results showed the reliability and robustness of the proposed method with high accuracy, even for noisy images with heterogeneous and weak boundaries.

关键词： Oil spill detection synthetic aperture radar images level set method dark spot detection multi-objective grey wolf optimization hierarchical region-based level-set method

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