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Providing an energy efficient UAV BS positioning mechanism to improve wireless connectivity

AD HOC NETWORKS 2025年 170卷

作者： Pasandideh, Faezeh Najafzadeh, Alireza da Costa, Joao Paulo Javidi Santos, Giovanni Almeida de Lima, Daniel Valle de Freitas, Edison Pignaton Fed Univ Rio Grande do Sul UFRGS Dept Comp Sci Porto Alegre Brazil Hamm Lippstadt Univ Appl Sci Dept Lippstadt 2 Lippstadt Germany Grad Sch Appl Res North Rhine Westphalia PK NRW D-44801 Bochum Germany Iran Univ Sci & Technol IUST Sch Comp Engn Tehran Iran IFCE Postgrad Program Telecommun Engn PPGET Fortaleza Brazil Halmstad Univ Sch Informat Technol Halmstad Sweden

As wireless communication continues to advance, the move towards Sixth-Generation (6G) networks has heightened the need for faster data speeds and reliable connections, prompting new approaches to connectivity. In scenarios such as natural disasters, where Ground Base Stations (GBSs) may be compromised, the use of Unmanned Aerial Vehicles (UAVs) has become increasingly important. A promising approach is to deploy low- altitude UAVs equipped with compact Base Stations (BSs) to reestablish essential communication networks and offer temporary coverage. However, identifying the optimal locations for these UAV-BSs presents a complex challenge. This paper proposes an innovative solution using UAVs as base stations (UAV-BSs) and introduces a Mixed-Integer Non-Linear Programming (MINLP) optimization model to position UAV-BSs based on real-time demand and network conditions. Traditional methods struggle with the complexity of UAV-BS deployment, so a novel algorithm combining the jaya optimization technique is used. Extensive experiments show this approach maximizes network coverage and connectivity while minimizing UAV-BS power consumption, outperforming other methods in placement accuracy, power usage, packet loss, and latency. The algorithm also adapts to varying network conditions, making it a valuable tool for optimizing UAV-BS locations in dynamic environments.

关键词： UAV base station jaya optimization algorithm Deployment problem Non-linear optimization

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optimization of radioactive sources to achieve the highest precision in three-phase flow meters using jaya algorithm

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APPLIED RADIATION AND ISOTOPES 2018年 139卷 256-265页

作者： Roshani, G. H. Karami, A. Khazaei, A. Olfateh, A. Nazemi, E. Omidi, M. Kermanshah Univ Technol Elect Engn Dept Kermanshah Iran Razi Univ Mech Engn Dept Kermanshah Iran Islamic Azad Univ Kermanshah Branch Young Researchers & Elite Club Kermanshah Iran Nucl Sci & Technol Res Inst Tehran Iran

Gamma ray source has very important role in precision of multi-phase flow metering. In this study, different combination of gamma ray sources ((Ba-133-Cs-137), (Ba-133-Co-60), ((AmCs)-Am-241-Cs-137), (Am-241-Co-60), (Ba-133-Am-241) and ((CoCs)-Co-60-Cs-137)) were investigated in order to optimize the three-phase flow meter. Three phases were water, oil and gas and the regime was considered annular. The required data was numerically generated using MCNP-X code which is a Monte-Carlo code. Indeed, the present study devotes to forecast the volume fractions in the annular three-phase flow, based on a multi energy metering system including various radiation sources and also one NaI detector, using a hybrid model of artificial neural network and jaya optimization algorithm. Since the summation of volume fractions is constant, a constraint modeling problem exists, meaning that the hybrid model must forecast only two volume fractions. Six hybrid models associated with the number of used radiation sources are designed. The models are employed to forecast the gas and water volume fractions. The next step is to train the hybrid models based on numerically obtained data. The results show that, the best forecast results are obtained for the gas and water volume fractions of the system including the (Am-241-Cs-137) as the radiation source.

关键词： Three-phase flow Gamma ray sources Artificial neural network jaya optimization algorithm MCNP-X code

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Application of jaya algorithm for the optimization of machining performance characteristics during the turning of CFRP (epoxy) composites: comparison with TLBO, GA, and ICA

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ENGINEERING WITH COMPUTERS 2017年第3期33卷 457-475页

作者： Abhishek, Kumar Kumar, V. Rakesh Datta, Saurav Mahapatra, Siba Sankar ICFAI Fdn Higher Educ Fac Sci & Technol Dept Mech Engn Hyderabad 500082 Telangana India Natl Inst Technol Dept Mech Engn Rourkela 769008 India

With the widespread application of carbon fibre-reinforced polymer (CFRP) composites, mostly in defence, automotive, and aerospace industries, the machining of those materials has become a major concern today. As the machinability of those composites differs from the conventional metals, a proper understanding of process behaviour and identification of the favourable machining environment (optimal setting of process parameters) are indeed necessary to improve product quality. The present work highlights the application potential of a multi-response optimization route by integrating nonlinear regression modelling, fuzzy inference system (FIS) in combination with the jaya optimization algorithm, for the selection of optimal process parameter setting during the machining (turning) of carbon fibre-reinforced (epoxy) composites. Experiments have been carried out in consideration with spindle speed, feed rate, and depth of cut as process control parameters, whereas material removal rate (MRR), roughness average (R (a)), and net cutting force have been treated as machining performance characteristics. Attempt has been made to identify the best setting of process parameters for optimizing aforesaid output responses, simultaneously. The result of the jaya algorithm has also been compared to that of TLBO (teaching-learning-based optimization) algorithm. In addition to this, the result obtained thereof has also been compared to that of two evolutionary optimization algorithms viz., GA (genetic algorithm) and ICA (imperialist competitive algorithm). Good agreement has been observed amongst the obtained results. The aforesaid case experimental study thus exhibits the application potential of a newly developed jaya algorithm in the context of machining performance optimization during the turning of CFRP composites. The jaya algorithm is basically a parameter-less optimization algorithm which does not require any algorithm-specific parameter and hence easy to implement.

关键词： Carbon fibre-reinforced polymer (CFRP) Nonlinear regression modelling Fuzzy inference system (FIS) jaya optimization algorithm TLBO (teaching-learning-based optimization) GA (genetic algorithm) ICA (imperialist competitive algorithm)

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Optimal Walking Gait Generator for Biped Robot Using Modified jaya optimization Technique

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2020年第1期13卷 382-399页

作者： Ho Pham Huy Anh Tran Thien Huang VNU HCM Fac Elect & Elect Engn FEEE HCM Univ Technol Ho Chi Minh City Vietnam HCM City Univ Technol & Educ Fac Appl Sci FAS Ho Chi Minh City Vietnam

This paper treats the optimization of the biped walking trajectory that can be used as a reference trajectory for control. The biped robot is modeled as a kinetic chain of 11 links connected by 10 joints. The inverse kinematics of the biped is derived for the specified positions of the hips and feet. The objective is to optimize the biped robot able to stably and naturally walking with preset foot-lift magnitude (or preset hip-shift, or preset step-length). The stability of the biped robot is quantified by the distance between the ZMP and the foot center in the step cycle, which represents the first objective function. Additionally, for the biped robot to follow the preset foot-lift value, the difference between the magnitude of foot-lift value and the foot-lift preset value represents the second objective function. Specifically, we minimize the value of the two objective functions by considering the gait parameters of biped robot as variables. The new jaya optimization algorithm is innovatively applied to optimize the biped gait parameters as to ensure the biped robot walking robustly and steadily. The efficiency of the proposed jaya-based identification method is compared with the Genetic algorithm (GA), the Particle Swarm optimization (PSO), the Central Force optimization (CFO) and improved Differential Evolution (DE) [Modified Differential Evolution (MDE)] algorithms. The simulation results tested on the real small-sized biped robot system HUBOT-4 demonstrate that the novel proposed algorithm offers an efficient and stable gait for biped robot with precise foot-lift value. (C) 2020 The Authors. Published by Atlantis Press SARL.

关键词： Uncertain nonlinear biped robot system Genetic algorithm (GA) Particle Swarm optimization (PSO) jaya optimization algorithm Central Force optimization (CFO) algorithm Modified Differential Evolution (MDE) algorithm Gait optimization for biped robot Zero moment point (ZMP) concept

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Online load frequency control in wind integrated power systems using modified jaya optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2019年 77卷 212-228页

作者： Pradhan, Chittaranjan Bhende, Chandrashekhar N. Indian Inst Technol Sch Elect Sci Bhubaneswar 751013 India

The jaya optimization algorithm is recognized as a simple and faster population-based heuristic search algorithm. However, due to the absence of algorithm-specified parameter, its performance may degrade in terms of convergence speed and obtain the optimal value for the real-world complex optimization problems which are usually nonlinear and non-differentiable in nature. To deal with the aforesaid scenarios, a modified jaya optimization algorithm (MJOA) is proposed by considering a weight parameter in the search process. Two methods are proposed on the basis of the selection of weight parameter, one is a linear weight (LW) and the other is a fuzzy logic based mechanism. It is found that MJOA is quite accurate and faster in convergence for complex problems. The proposed MJOA has used for online tuning the controller parameters of automatic generation control (AGC) of wind integrated power system. Further, a frequency deviation tolerance concept is proposed to reduce the number of executions of MJOA. The proposed methodology helps in recovering the frequency excursions of the power system with a faster convergence performance and requirements of less computational burden. Such features are very important in smart-grid operational necessities for improving its stability performance. The real-time simulation studies are carried out on an embedded platform using xPC target board of the wind farm integrated IEEE-39 bus test system.

关键词： Fuzzy logic jaya optimization algorithm Load frequency control Real-time optimization Wind integrated power system

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Bi-level optimization of resource allocation and appliance scheduling in residential areas using a Fog of Things (FOT) framework

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第1期27卷 219-229页

作者： Jain, Ankur Jatoth, Chandrashekar Gangadharan, G. R. Natl Inst Technol Dept Comp Sci & Engn Hamirpur India Natl Inst Technol Dept Comp Applicat Tiruchirappalli India

Fog computing has become a state-of-art technology for cloud applications in collaboration with physical IOT devices on the edge of the *** propose a Fog of Things (FOT) framework for optimized resource allocation to manage issues of over demanding and load balancing. The work discussed here is based on the residential demand side model for the areas that generate large number of requests per hour and resource allocation to these requests that require large amount of time and resources. In our FOT Framework, we have two main layers: fog layer and consumer layer. The fog layer performs the resource allocation in optimal time using jaya optimization algorithm. The consumer layer makes decision for selection of particular appliances in residential buildings using a Multiple Knapsack algorithm. Both layers act as players following the extensive form of the game theory approach to share their moves to each other.

关键词： Fog computing Resource allocation Task scheduling jaya optimization algorithm Multiple knapsack problem

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An improved H infinity controller using hybrid optimization for PV integrated MMC-HVDC system

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第2期83卷 3917-3943页

作者： Ali, Qazi Waqar Inst Appl Technol Abu Dhabi U Arab Emirates

Generally, circulating current is generated in a Modular Multilevel Converter (MMC) by fluctuations in the capacitor voltage of sub-modules. Therefore, this research work seeks to build the Photo Voltaic (PV) integrated MMC-High-Voltage Direct Current (HVDC) systems with optimal H-infinity controller which is appropriate for 3-phase system. To verify the unstable voltages of sub-modules, the reference current is initially set to zero, as well as the unstable currents in each phase are monitored with a current sensor as well as contrasted to the reference current, resulting in the generation of an incorrect circulating current. The main contribution is to reduce the error among the reference current as well as the actual circulating current. In the H-infinity controller, the gains are optimally tuned via a new hybrid algorithm referred as jaya optimization Insisted Explored Chimp (JOI-EC) algorithm, which is the hybridized version of the jaya optimization algorithm (JOA) and the Chimp optimization algorithm (ChOA). Moreover, the performance of the proposed model is examined through control analysis, converter performance analysis, as well as capacitor voltage analysis in addition to the circulating current. The result of simulation shows the robust performance of the proposed controller with respect to minimum circulating current, voltage, and stability analysis.

关键词： MMC H-infinity controller jaya optimization insisted explored chimp Circulating current jaya optimization algorithm Chimp optimization algorithm

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Identification and optimization of the operator's hand and a haptic device dynamic, using artificial intelligence methods

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INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2023年第6期11卷 3052-3061页

作者： Mashayekhi, Ahmad Mashayekhi, Mostafa Siciliano, Bruno Sirjan Univ Technol Mech Engn Dept Sirjan Iran Vali E Asr Univ Rafsanjan Dept Civil Engn Rafsanjan Iran Univ Naples Federico II Dept Elect Engn & Informat Technol I-80125 Naples Italy

A haptic device (HD) is used to simulate a virtual object (VO) for its operator. Since most real objects can be simulated with a spring and damper, the VO is also implemented as a spring and damper in the discrete domain. On one side, the HD should have a small effective mass and friction so that its dynamics can be neglected in contrast to the dynamics of the VO. On the other side, even the small dynamics of the HD or the operator's hand affect the stability and performance of the robot. So the dynamic model of the HD and the operator's hand should be determined accurately. In this research, the mentioned dynamics have been identified on a HD using different artificial intelligence methods of ray optimization method, jaya optimization algorithm, crow search algorithm, Big-Bang Big-Crunch, adaptive dimensional search algorithm, and artificial bee colony optimization (ABCO). To this purpose, the theoretical stability boundary is presented as a function of the HD and the hand's dynamic parameters, the stiffness and the damping coefficient of the VO, sampling time, and time delay. Simultaneously, with some experiments on the KUKA light weight robot IV (LWR IV), the boundary of experimental stability has been achieved in different situations. Then, using the mentioned methods the values of the HD and the hand's dynamic parameters are obtained in such a way that the error between the theoretical and the experimental stability boundaries is minimal. Studies show that for predicting the HD parameters all mentioned methods have somehow the same and good accuracy, while the BBBC method has the minimum runtime. For predicting the HD and the operator's hand dynamic parameters, the BBBC method has the minimum error and runtime.

关键词： Haptic device Stability Ray optimization method jaya optimization algorithm Crow search algorithm Big-bang big-crunch Adaptive dimensional search algorithm Artificial bee colony optimization

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optimization free of algorithm-specific control parameters for Power System Stabilizer tuning 61

Optimization free of algorithm-specific control parameters f...

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61st IEEE Annual International Scientific Conference on Power and Electrical Engineering of Riga-Technical-University (RTUCON)

作者： Gonzalez, M. Ramirez Sevilla, F. R. Segundo Korba, P. ZHAW Zurich Univ Appl Sci Inst Energy Syst & Fluid Engn Winterthur Switzerland

ISBN: (纸本)9781728195100

A settings-free and derivative-free optimization technique, called jaya optimization algorithm, is investigated in this paper for the design of simple fuzzy PSSs (FPSSs) in a multimachine power system. Performance of this technique during the optimization process is compared to the one exhibited by a Bat optimization algorithm based approach. Considering a set of test cases for illustrative purposes, FPSSs tuned with both of these algorithms are applied to the study system and their performance in damping inter-area and local oscillations is analyzed against some commonly used lead-lag structures, including multi-band PSSs.

关键词： Power system stabilizer fuzzy logic parameter tuning settings-free optimization jaya optimization algorithm

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Optimal Operation of Synchronous Motor Using Particle Swarm optimization and jaya Techniques 21

Optimal Operation of Synchronous Motor Using Particle Swarm ...

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21st International Middle East Power Systems Conference (MEPCON)

作者： Abdelwanis, Mohamed, I Selim, F. Kafrelsheikh Univ Fac Engn Elect Engn Dept Kafrelsheikh Egypt

ISBN: (纸本)9781728152899

This paper concerns with the application of optimized operation of synchronous motor problem (SM) in industrial power system applications. This represents an active area of research, and many modeling and computational techniques are periodically being proposed for solving these problems. Therefore, this paper proposes optimization procedure for the operation of (SM) using Particle Swarm optimization (PSO) and jaya optimization techniques. The optimal operation of synchronous motor by using PSO and jaya is suggested with constant outputs to minimize the motor armature current, maximize the motor efficiency, minimize the synchronous motor input apparent power and reactive power with reducing of the copper losses. The analysis of results explains the benefits of using the optimization methods (PSO, jaya) with respect to the traditional method especially jaya optimization results. The implementation work (practical) is carried out in laboratory of faculty of engineering in Kafrelsheikh University.

关键词： jaya optimization algorithm Optimal operation Particle swarm optimization technique (PSO) Synchronous motor (SM)

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