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Locating optimal position of artificial recharge wells in aquifer using grey wolf optimization algorithm and isogeometric numerical method

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APPLIED WATER SCIENCE 2022年第7期12卷 164-164页

作者： Poursalehi, F. Akbarpour, A. Hashemi, S. R. Birjand Univ Water Resources Engn Birjand Iran Birjand Univ Dept Civil Engn Birjand Iran Birjand Univ Dept Water Engn Birjand Iran

The construction of injection wells is one of the direct methods of artificial recharge and determining their optimal location is one of the important issues that are discussed in the topics of projects related to the rehabilitation of aquifers. In this research, a simulation-optimization model was proposed to determine the optimal location of injection wells using the Isogeometric analysis (IGA) numerical model and the grey wolf optimization algorithm (GWO). In this regard, first, a groundwater model based on Isogeometric analysis was created to simulate groundwater flow in a hypothetical aquifer. Finally, after ensuring the accuracy of the simulator model, the optimal location of 10 injection wells was evaluated under two scenarios based on different values of hydraulic conductivity and specific yield. The accuracy of the simulation model is computed based on three error criteria ME, MAE and RMSE were the evaluation criteria which equaled -0.96%, 1.11%, and 0.0146 m, respectively. The achieved results showed that the Isogeometric analysis model has high accuracy. The results of the IGA-GWO model indicated that after constructing injection wells in the optimal location, the groundwater table on average in 10 injection wells rises more than 50 cm in both scenarios. The results also showed that due to the change in aquifer hydraulic conductivity and specific yield in different regions and the defined boundary conditions in the problem, the optimal location of injection wells are in regions with more hydraulic conductivity and more specific yield. Also, injection in regions with more drops will increase the groundwater table.

关键词： Artificial recharge Simulation-optimization model grey wolf algorithm Isogeometric analysis model Groundwater table

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Anomaly-based intrusion detection system using multi-objective grey wolf optimisation algorithm

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年第9期11卷 3735-3756页

作者： Alamiedy, Taief Alaa Anbar, Mohammed Alqattan, Zakaria N. M. Alzubi, Qusay M. Univ Sains Malaysia Natl Adv IPv6 Ctr Excellence NAv6 Usm Penang 11800 Malaysia

The rapid development of information technology leads to increasing the number of devices connected to the Internet. Besides, the amount of network attacks also increased. Accordingly, there is an urgent demand to design a defence system proficient in discovering new kinds of attacks. One of the most effective protection systems is intrusion detection system (IDS). The IDS is an intelligent system that monitors and inspects the network packets to identify the abnormal behavior. In addition, the network packets comprise many attributes and there are many attributes that are irrelevant and repetitive which degrade the performance of the IDS system and overwhelm the system resources. A feature selection technique helps to reduce the computation time and complexity by selecting the optimum subset of features. In this paper, an enhanced anomaly-based IDS model based on multi-objective grey wolf optimisation (GWO) algorithm was proposed. The GWO algorithm was employed as a feature selection mechanism to identify the most relevant features from the dataset that contribute to high classification accuracy. Furthermore, support vector machine was used to estimate the capability of selected features in predicting the attacks accurately. Moreover, 20% of NSL-KDD dataset was used to demonstrate effectiveness of the proposed approach through different attack scenarios. The experimental result revealed that the proposed approach obtains classification accuracy of (93.64%, 91.01%, 57.72%, 53.7%) for DoS, Probe, R2L, and U2R attack respectively. Finally, the proposed approach was compared with other existing approaches and achieves significant result.

关键词： Intrusion detection system Feature selection Multi-objective optimisation Swarm intelligence grey wolf algorithm Support vector machine Classification

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grey wolf algorithm and Multi-Objective Model for the Manycast RSA Problem in EONs

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INFORMATION 2019年第12期10卷 398-398页

作者： Xuan, Hejun Lin, Lidan Qiao, Lanlan Zhou, Yang Xinyang Normal Univ Sch Comp Sci & Technol Xinyang 464000 Peoples R China Luo He Vocat Technol Coll Dept Infomat Engn Luohe 462000 Henan Peoples R China

Manycast routing and spectrum assignment (RSA) in elastic optical networks (EONs) has become a hot research field. In this paper, the mathematical model and high efficient algorithm to solve this challenging problem in EONs is investigated. First, a multi-objective optimization model, which minimizes network power consumption, the total occupied spectrum, and the maximum index of used frequency spectrum, is established. To handle this multi-objective optimization model, we integrate these three objectives into one by using a weighted sum strategy. To make the population distributed on the search domain uniformly, a uniform design method was developed. Based on this, an improved grey wolf optimization method (IGWO), which was inspired by PSO (Particle Swarm Optimization, PSO) and DE (Differential Evolution, DE), is proposed to solve the maximum model efficiently. To demonstrate high performance of the designed algorithm, a series of experiments are conducted using several different experimental scenes. Experimental results indicate that the proposed algorithm can obtain better results than the compared algorithm.

关键词： manycast RSA EONs multi-objective optimization grey wolf algorithm

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Multi-response optimization of CuZn39Pb3 brass alloy turning by implementing grey wolf algorithm

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FRATTURA ED INTEGRITA STRUTTURALE-FRACTURE AND STRUCTURAL INTEGRITY 2019年第50期13卷 584-594页

作者： Fountas, Nikolaos Koutsomichalis, Angelos Kechagias, John D. Vaxevanidis, Nikolaos M. Sch Pedag & Technol Educ Dept Mech Engn Educators Lab Mfg Proc & Machine Tools ASPETE Campus GR-14121 N Heraklion Greece Hellen Air Force Acad Fac Aeraipace Studies GR-19005 Dekelia AFB Greece Technol Educ Inst Thessaly Mech Engn Dept TEI Campus GR-41110 Larisa Greece

Machinability of engineering materials is crucial for industrial manufacturing processes since it affects all the essential aspects involved, e.g. workload, resources, surface integrity and part quality. Two basic machinability parameters are the surface roughness, closely associated with the functional and tribological performance of components, and the cutting forces acting on the tool. Knowledge of the cutting forces is needed for estimation of power requirements and for the design of machine tool elements, tool-holders and fixtures, adequately rigid and free from vibration. This work investigates the influence of cutting conditions on machinability indicators such as the main cutting force Fc and surface roughness parameters Ra and Rt when longitudinally turning CuZn39Pb3 brass alloy. Full quadratic regression models were developed to correlate the machining conditions with the imparted machinability characteristics. Further on, an advanced artificial grey wolf optimization algorithm was implemented to optimize the aforementioned responses with great success in finding the final optimal values of the turning parameters.

关键词： Turning Surface roughness Cutting forces Multi-parameter analysis Optimization grey wolf algorithm

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grey wolf algorithm-Based Clustering Technique

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JOURNAL OF INTELLIGENT SYSTEMS 2017年第1期26卷 153-168页

作者： Kumar, Vijay Chhabra, Jitender Kumar Kumar, Dinesh Thapar Univ Comp Sci & Engn Dept Patiala Punjab India Natl Inst Technol Dept Comp Engn Kurukshetra Haryana India GJUS & T Comp Sci & Engn Dept Hisar Haryana India

The main problem of classical clustering technique is that it is easily trapped in the local optima. An attempt has been made to solve this problem by proposing the grey wolf algorithm (GWA)-based clustering technique, called GWA clustering (GWAC), through this paper. The search capability of GWA is used to search the optimal cluster centers in the given feature space. The agent representation is used to encode the centers of clusters. The proposed GWAC technique is tested on both artificial and real-life data sets and compared to six well-known metaheuristic-based clustering techniques. The computational results are encouraging and demonstrate that GWAC provides better values in terms of precision, recall, G-measure, and intracluster distances. GWAC is further applied for gene expression data set and its performance is compared to other techniques. Experimental results reveal the efficiency of the GWAC over other techniques.

关键词： grey wolf algorithm data clustering K-means metaheuristics

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Optimal power flow with FACTS devices using a Novel grey wolf algorithm 3

Optimal power flow with FACTS devices using a Novel Grey Wol...

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3rd IEEE International Conference on Science Technology Engineering & Management (ICONSTEM)

作者： Singh, Madhu Dutta, Susanta Roy, Provas Kumar Dr BC Roy Engn Coll Dept Elect Engn Durgapur India Kalyani Govt Engn Coll Dept Elect Engn Nadia India

ISBN: (纸本)9781509048557

In this paper a new novel meta-heuristic approach grey wolf algorithm is employed for solving OPF problems mainly focusing on Fuel cost minimization (with and without valve point effect) and transmission loss minimization with the incorporation of FACTS devices TCSC and TCPS. GWO approach is independent of gradient of the objective function for searching global optimum solution, focusing on behaviour of wolves in nature their hunting mechanism and leadership hierarchy, the proposed algorithm is implementedand tested successfully on IEEE 30 bus test power system. the simulation result indicates comparatively superior performance of GWO in terms of speed and global optimum solution in comparison to newly developed methods mentioned in literature for OPF.

关键词： grey wolf algorithm Static Flexible AC transmission system Optimal Power flow Thyristor controlled series capacitor(TCSC) and Thyristor controlled phase shifter(TCPS)

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Multiple Objective Scheduling Optimization algorithm Based on grey wolf Optimization and Variable Neighborhood Search algorithm 4

Multiple Objective Scheduling Optimization Algorithm Based o...

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4th International Conference on Electronic Information Engineering and Computer Technology, EIECT 2024

作者： Liu, Jiannan Liu, Wencheng Wang, Ting Zhang, Shengnan School of Software Shenyang University of Technology Liaoning Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9798331528850

Multi-objective scheduling is a key problem in the field of scheduling optimization such as warehouse logistics. Aiming at the problems of insufficient local search ability and difficult multi-objective trade-off in the existing methods, this article proposes an improved GWO-VNS algorithm. According to existing research, significant enhancements in local search capability and multi-objective optimization performance are achieved by incorporating the variable neighborhood search algorithm and applying crossover and mutation operations. The comparative experimental results show that the IGD index of GWO-VNS algorithm is reduced by 33%, and the HV index is increased by 11%, which verifies its significant advantages in multi-objective scheduling problems. © 2024 IEEE.

关键词： grey wolf algorithm Multi-objective Optimization Variable Neighborhood Search

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Comparative Evaluation of Genetic and grey wolf algorithms for Building Energy Optimization 4th

Comparative Evaluation of Genetic and Grey Wolf Algorithms f...

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4th International Conference on Advanced Engineering Optimization Through Intelligent Techniques

作者： Chaturvedi, Shobhit Rajasekar, Elangovan Sharma, Kratika Pandit Deendayal Energy Univ Sch Technol Dept Civil Engn Gandhinagar India Indian Inst Technol Dept Architecture & Planning Roorkee Uttar Pradesh India Indian Inst Informat Technol Comp Sci Dept Pune Maharashtra India

ISBN: (纸本)9789819796618;9789819746545;9789819746538

Genetic algorithm (GA) and grey wolf Optimization (GWO) can aid building designers in optimizing construction materials, glazing, orientation, and air conditioner sizing. However, their suitability for building optimization via Energy-Plus simulation lacks clear guidance. This paper utilizes GA and GWO to minimize Building Annual Cooling Energy Consumption (ACEC) in Ahmedabad and New Delhi, exploring 196,830 design options through modifications in wall construction, window design and size, roof insulation, building orientation, air conditioner sizing, and cooling setpoints. Both algorithms run for 20 generations with 50 agents each, and their convergence, runtimes, and designs are analysed. In Ahmedabad, GA achieves a 50.72% ACEC, while GWO achieves a higher 51.78% reduction with shorter runtimes. In Ahmedabad, GA suggests Cellular Concrete walls, GWO prefers Aerated Concrete. Both agree on 5% window-wall ratio, Triple Glazing Windows with 0.5-m overhang, similar AC sizes except Bedroom 3. They differ in roof insulation (GA: Glass wool, GWO: Expanded Polystyrene). In New Delhi, GA and GWO closely align, recommending Aerated Concrete walls, Triple Glazing Windows with 5% window-wall ratio, a 0.5-m overhang. Both GA and GWO propose Expanded Polystyrene insulation, AC sizes differ in Bedrooms 1, 2, 3, and 4. GA has shorter runtimes-0.91 h in Ahmedabad and 0.93 h in New Delhi-than GWO's 1.78 and 2.05 h. The faster convergence of GWO, attributed to its broader search space, underscored its potential as an efficient optimization strategy, promoting sustainable building design.

关键词： Energy efficient building design Genetic algorithm grey wolf algorithm EnergyPlus Matlab EPlauncher

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Optimisation of commercial bus body frame based on the improved grey wolf and Monte Carlo simulation algorithm

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INTERNATIONAL JOURNAL OF VEHICLE PERFORMANCE 2024年第1期10卷 24-49页

作者： Yang, Xiujian Gan, Jinlin Kunming Univ Sci & Technol Fac Transportat Engn 727 South Jingming Rd Kunming 650500 Peoples R China

This work aims to study the method of design optimisation of the bus body frame orienting the frontal crashworthiness. The optimal design variables are preliminarily determined based on the frontal crashworthiness analysis. Based on the analysis of correlations between the concerned responses and design variables and the comprehensive contribution analysis, the design variables for optimisation are finally determined. The surrogate model is established by the Latin hypercube design of experiments and the response surface method. The grey wolf (GWO) algorithm is improved by introducing the method of generating initialisation by the Tent mapping and improving the convergence factor by the Sigmoid function. By the improved GWO algorithm, the deterministic optimisation and reliability optimisation are performed and evaluated. The results of finite element analysis reveal that the proposed optimisation scheme can be effectively improved the performance of frontal crashworthiness of the bus body frame with high reliability.

关键词： design optimisation bus body frame crashworthiness reliability optimisation TOPSIS grey wolf algorithm

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Information Technology for Parametric Optimization of Fuzzy Systems Based on Hybrid grey wolf algorithms

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SN Computer Science 2022年第6期3卷 1-18页

作者： Kozlov, Oleksiy V. Kondratenko, Yuriy P. Skakodub, Oleksandr S. Petro Mohyla Black Sea National University 10 68th Desantnykiv Str. Mykolaiv 54003 Ukraine

In recent years, bioinspired multi-agent techniques have become very popular for solving real-life engineering optimization problems. One of the promising areas of using these methods is their application for the development and optimization of intelligent systems, in particular, fuzzy control and decision-making systems. This paper presents the development and study of information technology for parametric optimization of different types of fuzzy systems (FS) based on the combination of improved grey wolf algorithm and local search techniques. The proposed information technology allows to conduct effective optimization of various parameters of FSs, finding the optimal solution of the problem, and, at the same time, has a higher convergence rate compared with the technologies that use basic and improved grey wolf algorithms. The effectiveness study of the proposed information technology is carried out at a simulation experiment on parametric optimization of a fuzzy control system for the quadrotor unmanned aerial vehicle. The obtained simulation results confirm the high efficiency of the developed information technology based on hybrid improved grey wolf algorithm, as well as the feasibility of its use for parametric optimization of the fuzzy control and decision-making systems of various types and configurations. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Bioinspired multi-agent techniques Fuzzy control and decision-making systems grey wolf algorithm Parametric optimization information technology Quadrotor unmanned aerial vehicle

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