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An improved grey wolf algorithm and its localization research in complex indoor environments

SCIENTIFIC REPORTS 2025年第1期15卷 1-23页

作者： Li, Bing Hao, Yanxin Cui, Yiyang Chai, Xingshao Zhou, Jingmei Liu, Chungang Hebei Normal Univ Coll Combust Engn Shijiazhuang 050024 Peoples R China Hebei Prov Key Lab Informat Fus & Intelligent Cont Shijiazhuang 050024 Peoples R China Hebei Prov Innovat Ctr Wireless Sensor Network Dat Shijiazhuang 050024 Peoples R China

In complex indoor environments, traditional localization methods often suffer from non-line-of-sight (NLOS) and multipath problems, which lead to unsolvable or incorrectly solved mathematical localization models, thereby limiting localization accuracy. A localization method based on swarm intelligence optimization has been proposed to address this issue. The swarm intelligence optimization algorithm does not require solving matrix inversions and transforms the localization problem into a function optimization problem, which can obtain approximate optimal solutions. Nevertheless, optimization algorithms are beset with issues like sluggish convergence speed and proneness to getting trapped in local optima, thereby failing to satisfy the current practical requirements for localization. This paper proposes a new method that applies the grey wolf optimization (GWO) algorithm to ultra-wideband (UWB) indoor localization to enhance localization accuracy. It improves the GWO algorithm with four strategies. Firstly, a small-area optimization strategy near the target point is proposed. The Chan algorithm is adopted for initial tag localization, and the initial localization result is taken as a constraint to construct the search area of the GWO algorithm, thereby reducing the large space region to a small space region and enhancing optimization efficiency. Secondly, an improved Tent mapping, a nonlinear convergence factor, a fitness-weighted location update strategy, and an out-of-bounds reflection mechanism are designed to improve the GWO algorithm, referred to as the TIGWO algorithm. Finally, apply the TIGWO algorithm to determine the optimal location of the tag. The experimental results indicate that the proposed algorithm significantly enhances indoor localization accuracy. Compared to the Chan, Chan-Taylor, PSO, WOA, and GWO algorithms, the average localization accuracy has been enhanced by 59.65%, 63.41%, 40.97%, 45.97%, and 35.44%, respectively. In an equipment warehouse

关键词： Ultra-wideband technology 3D indoor localization Swarm algorithm grey wolf optimization algorithm Localization accuracy

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Performance optimization of high-rise residential buildings in cold regions considering energy consumption

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DISCOVER APPLIED SCIENCES 2025年第2期7卷 1-14页

作者： Song, Liwei Jilin Technol Coll Elect Informat Acad Affairs Off Jilin 132021 Peoples R China

With the acceleration of urbanization, high-rise residential buildings has become a significant aspect of urban living. However, while high-rise residential buildings provide much housing, they also bring significant energy consumption. To raise the energy utilization efficiency of high-rise residential buildings, reduce energy consumption, and achieve sustainable development, this study focuses on high-rise residential buildings in cold regions. Through methods such as parametric modeling, joint simulation of building performance, multi-objective optimization algorithms, and improved grey wolf optimization algorithms, multi-objective optimization experiments are conducted to achieve optimal energy-saving effects. The outcomes denote that the average energy consumption of buildings remains at around 20.5 kW h/m2, and the maximum value of the last generation thermal comfort solution set is maintained at 62%, while the minimum value is maintained at 58%. The improved grey wolf optimization algorithm reduces training time, has better predictive ability, and can more accurately characterize changes in energy consumption of high-rise buildings. This study provides practical design methods and strategy references for high-rise residential buildings in the design phase by analyzing data, mining patterns, and summarizing design strategies.

关键词： Energy consumption High-rise residential buildings grey wolf optimization algorithm Multi-objective optimization Enclosure structure

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Stochastic Energy Management Operation Strategy for High Penetrated Grid Connected Solar with Incorporation of Battery Storage System

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SMART GRIDS AND SUSTAINABLE ENERGY 2025年第1期10卷 1-11页

作者： Semwal, Gourav Sharma, Sachin Rawat, Tanuj Sharma, Gulshan Bansal, Ramesh C. Graph Era Deemed be Univ Dept Elect Engn Dehra Dun India Manipal Acad Higher Educ Elect & Elect Engn Manipal Inst Technol Manipal Karnataka India Newcastle Univ Sch Engn Newcastle Upon Tyne England Univ Johannesburg Dept Elect Engn Technol Johannesburg South Africa Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates Univ Pretoria Dept Elect Elect & Comp Engn Pretoria South Africa

The present distribution systems are heading towards smart distribution systems to attain large socio economic benefits. For achieving these benefits, the distribution system will include the practical aspect of the flexible modern technologies like renewable energy based distributed generators, demand response and battery energy storage system to manage load governance. There is source of uncertainty present in the non-dispatch able based distributed generations (DGs) that affects the operation schedule of distribution system. Therefore, this paper proposes a novel operation strategy for battery energy storage in coordination with uncertain large scale Photo-voltaic system based DG for distribution system. The optimal discharging and charging plans of battery energy storage for accommodation of uncertain photovoltaic are condition to the constraint like nodal power balance, feeder current limit and node voltage limit etc. grey wolf optimization algorithm (GWO) is developed for analyzing the impact of multiple battery energy storage strategies, for controlling the demand deviation and node voltage of the distribution system. GWO algorithm is investigated on IEEE-33 bus radial distribution systems. The efficacy of the result shows the successful achieving the promised voltage profile and grid demand deviation profile.

关键词： Distributed generation Battery energy storage systems Photo-voltaic system grey wolf optimization algorithm

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optimization of Flight Endurance for Turboprop Air Taxis Using Metaheuristic algorithms

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INTERNATIONAL JOURNAL OF ENGINEERING 2025年第7期38卷 1685-1698页

作者： Talouki, I. Fozouni Toloei, A. Shahid Beheshti Univ Dept New Technol & Aerosp Engn Tehran Iran

This study aims to optimize the flight endurance of a 12-passenger turboprop air taxi using two metaheuristic optimization algorithms: grey wolf optimization (GWO) and Ant Colony optimization (ACO). Initially, the gradient descent method was employed to estimate the aircraft's maximum weight. Subsequently, the aircraft's performance characteristics were utilized as design variables and flight endurance was optimized under specific constraints without altering the physical structure of the aircraft. The optimization process was implemented, and the results were evaluated and compared in terms of performance and efficiency. This research demonstrated that the two mentioned algorithms, utilizing random and collective strategies, were able to enhance the aircraft's efficiency. Additionally, the optimization of flight endurance for three real aircraft Piper, Beechcraft, and Bombardier was examined compared to their original endurance. In this context, the Ant Colony optimization algorithm exhibited better performance than the grey wolf optimization algorithm, which could have a positive impact on flight operations without refueling or the process of finding alternative airports.

关键词： Air Taxi optimization Gradient Descent grey wolf optimization algorithm Ant Colony optimization algorithm

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Robust Control Frameworks for Heave Disturbance Suppression in Offshore Managed Pressure Drilling Environments

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IEEE ACCESS 2025年 13卷 79137-79149页

作者： Rehman, Atif Ghias, Rimsha Ahmad, Iftikhar Sherazi, Hammad Iqbal NUST Sch Interdisciplinary Engn & Sci SINES Islamabad 44000 Pakistan NUST SEECS Islamabad 44000 Pakistan Qassim Univ Coll Engn Dept Elect Engn Buraydah 52571 Saudi Arabia

Managed Pressure Drilling (MPD) systems have transformed drilling operations by enabling precise wellbore pressure control, particularly in challenging conditions. Automatic MPD, leveraging advanced choke valve mechanisms, effectively mitigates pressure fluctuations caused by drill string oscillations induced by Heave Motion. This study introduces advanced nonlinear control strategies, including Sliding Mode Control (SMC) and Super-Twisting Sliding Mode Control (STSMC), to enhance pressure regulation. Controller gains are optimized using an improved grey wolf optimization algorithm, targeting the minimization of the Integral Time Absolute Error. Stability of the controllers is rigorously analyzed through Lyapunov criteria. Comprehensive performance evaluations via MATLAB/Simulink simulations, complemented by real-time validation using a C2000 Delfino MCU F28379D Launchpad, demonstrate the robustness and efficacy of the proposed control methodologies.

关键词： Drilling Mathematical models Fluctuations Sliding mode control Stability criteria Inductors Pressure control optimization Testing Robustness Advanced drilling techniques dynamic pressure regulation grey wolf optimization algorithm nonlinear control strategies

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Research on wheelchair form design based on Kansei engineering and GWO-BP neural network

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SCIENTIFIC REPORTS 2025年第1期15卷 1-25页

作者： Cai, Weilin Wang, Zhengyu Wang, Yi Zhou, Meiyu East China Univ Sci & Technol Sch Art Design & Media Shanghai 200237 Peoples R China

With the increasing emphasis on humanistic care in society, consumers are no longer only concerned about the functional needs of products but also about the spiritual, cultural, and emotional needs that products bring to people. This study proposes a wheelchair form design method based on the Kansei engineering approach, which integrates the evaluation grid method (EGM), grey wolf optimization (GWO) algorithm, and back propagation neural network (BPNN) technology. The aim is to explore the connection between wheelchair form design elements and user emotions and help industrial designers find designs with emotional preferences. In this study method, firstly, the collected wheelchair samples were evaluated using the EGM, extracting upper-level Kansei vocabulary driven by user attractiveness, middle-level original attractiveness items, and lower-level specific design elements and extracting nine sets of Kansei vocabulary mentioned frequently by users. Meanwhile, the morphological analysis method is used to construct a sample library of product morphological elements. Secondly, the semantic difference and factor analysis methods were used to analyze the ratings of 9 pairs of Kansei words, and the weights of Kansei factors were calculated to identify three critical Kansei demand factors. Thirdly, based on the analysis results of the orthogonal experiment, a conceptual plan for the wheelchair was constructed using computer-aided technology Rhinoceros 3D modelling software. Fourthly, the semantic difference method is used to collect users' ratings of critical Kansei words for wheelchair concept schemes, and the evaluation values of critical Kansei words are calculated by weighting. Fifth, a BPNN based on the GWO algorithm will establish a predictive model between wheelchair design elements and vital Kansei images. Finally, the predictive performance of BPNN and GWO-BPNN models will be compared to verify their superiority. The results indicate that the GWO-BPNN model has be

关键词： Wheelchair design Kansei engineering Evaluation grid method grey wolf optimization algorithm Back propagation neural network

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Optimal operation of new coastal power systems with seawater desalination based on grey wolf optimization

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ENERGY REPORTS 2023年 9卷 391-402页

作者： Gao, Yujie Yang, Hao Zhou, Bowen Chen, Xinyi Hu, Zhijun Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Key Lab Integrated Energy Optimizat & Secure Opera Shenyang 110819 Peoples R China Northeastern Univ Sch Resources & Civil Engn Shenyang 110819 Peoples R China

Seawater desalination is one of the effective means to efficiently consume renewable energy and to improve the flexibility of system control in the new power system. In the optimal operation of new power systems, seawater desalination is usually considered as an entirety, and a motor equivalent model is used to simulate the overall external characteristics of seawater desalination. The characteristics of multiple sets of motors with multiple equipment processes inside the seawater desalination are neglected, which makes operation control characteristics inaccurate. In order to solve the above-mentioned problems, this paper firstly studies a seawater desalination system composed of 12 motors, and introduces the specific parameters of these 12 motors in detail. Secondly, after comprehensively considering the constraints of water flow, water pressure, and other factors, three control strategies are proposed for the 12 motors, respectively. Then, a detailed mathematical model of seawater desalination units and an optimal operation model of a new power system with seawater desalination are established. The optimization model aims at the minimum operating cost of seawater desalination, and considers the constraints of practical problems comprehensively such as motor power requirements and water storage capacity. Finally, the grey wolf optimization algorithm (GWO) is used in this paper to solve the optimization problem. The simulation results show that the model proposed in this paper can precisely control each unit in the seawater desalination system and improve the economic benefits of the system. Compared with the Particle Swarm optimization algorithm (PSO) and the Moth-flame optimization algorithm (MFO), the algorithm used in this paper can find the global optimal solution, and has both faster convergence speed and wider practicability. (c) 2023 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/

关键词： Seawater desalination Detail model Optimal operation grey wolf optimization algorithm

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Improved multi-strategy adaptive grey wolf optimization for practical engineering applications and high-dimensional problem solving

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第10期57卷 277页

作者： Yu, Mingyang Xu, Jing Liang, Weiyun Qiu, Yu Bao, Sixu Tang, Lin Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China New York Inst Technol Dept Biol & Chem Sci New York NY 11568 USA

The grey wolf optimization (GWO) is a highly effective meta-heuristic algorithm leveraging swarm intelligence to tackle real-world optimization problems. However, when confronted with large-scale problems, GWO encounters hurdles in convergence speed and problem-solving capabilities. To address this, we propose an Improved Adaptive grey wolf optimization (IAGWO), which significantly enhances exploration of the search space through refined search mechanisms and adaptive strategy. Primarily, we introduce the incorporation of velocity and the Inverse Multiquadratic Function (IMF) into the search mechanism. This integration not only accelerates convergence speed but also maintains accuracy. Secondly, we implement an adaptive strategy for population updates, enhancing the algorithm's search and optimization capabilities dynamically. The efficacy of our proposed IAGWO is demonstrated through comparative experiments conducted on benchmark test sets, including CEC 2017, CEC 2020, CEC 2022, and CEC 2013 large-scale global optimization suites. At CEC2017, CEC 2020 (10/20 dimensions), CEC 2022 (10/20 dimensions), and CEC 2013, respectively, it outperformed other comparative algorithms by 88.2%, 91.5%, 85.4%, 96.2%, 97.4%, and 97.2%. Results affirm that our algorithm surpasses state-of-the-art approaches in addressing large-scale problems. Moreover, we showcase the broad application potential of the algorithm by successfully solving 19 real-world engineering challenges.

关键词： grey wolf optimization algorithm Search mechanism Inverse multiquadratic function Adaptive strategy Engineering design challenges

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Unsupervised intelligent system based on one class support vector machine and grey wolf optimization for IoT botnet detection

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年第7期11卷 2809-2825页

作者： Al Shorman, Amaal Faris, Hossam Aljarah, Ibrahim Univ Jordan King Abdullah II Sch Informat Technol Amman Jordan

Recently, the number of Internet of Things (IoT) botnet attacks has increased tremendously due to the expansion of online IoT devices which can be easily compromised. Botnets are a common threat that takes advantage of the lack of basic security tools in IoT devices and can perform a series of Distributed Denial of Service (DDoS) attacks. Developing new methods to detect compromised IoT devices is urgent in order to mitigate the negative consequences of these IoT botnets since the existing IoT botnet detection methods still present some issues, such as, relying on labelled data, not being validated with newer botnets, and using very complex machine learning algorithms. Anomaly detection methods are promising for detecting IoT botnet attacks since the amount of available normal data is very large. One of the powerful algorithms that can be used for anomaly detection is One Class Support vector machine (OCSVM). The efficiency of the OCSVM algorithm depends on several factors that greatly affect the classification results such as the subset of features that are used for training OCSVM model, the kernel type, and its hyperparameters. In this paper, a new unsupervised evolutionary IoT botnet detection method is proposed. The main contribution of the proposed method is to detect IoT botnet attacks launched form compromised IoT devices by exploiting the efficiency of a recent swarm intelligence algorithm called grey wolf optimization algorithm (GWO) to optimize the hyperparameters of the OCSVM and at the same time to find the features that best describe the IoT botnet problem. To prove the efficiency of the proposed method, its performance is evaluated using typical anomaly detection evaluation measures over a new version of a real benchmark dataset. The experimental results show that the proposed method outperforms all other algorithms in terms of true positive rate, false positive rate, and G-mean for all IoT device types. Also, it achieves the lowest detection time, whi

关键词： Internet of Things Anomaly detection Botnets Feature selection Intrusion detection system grey wolf optimization algorithm Novelty detection One class support vector machine

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Novel grey wolf optimization based on modified differential evolution for numerical function optimization

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APPLIED INTELLIGENCE 2020年第2期50卷 468-486页

作者： Luo, Jun Liu, Zewei Chongqing Univ Chongqing 400030 Peoples R China

grey wolf optimization algorithm (GWO) is a new swarm intelligence optimization algorithm proposed in recent years. Because of few control parameters and easy implementation, GWO is widely used in many fields. Compared with other common swarm optimization algorithms, it is more suitable for the global optimization problems. Nevertheless, the algorithm still has the shortcoming of low accuracy and slow convergence speed. In this paper, a novel hybrid optimization algorithm named MDE-GWO is proposed. Firstly, a JADE with opposition-based learning strategy algorithm (MDE) is embedded in GWO to enhance the ability of avoiding a local optimum. Notably, by introducing the opposition-based learning strategy, the search ability of the improved algorithm is increased greatly. Additionally, in order to balance the global and local search capabilities and speed up the convergence of the GWO algorithm, a leader moving-rate strategy is put forward. 28 typical benchmark functions are utilized to test the performance of the improved algorithm. The experimental results show that MDE-GWO has stronger advantages in search accuracy, stability and convergence speed in most cases.

关键词： grey wolf optimization algorithm Global optimization Opposition-based learning Leader moving-rate strategy Benchmark functions

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