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Data dissemination protocol for VANETs to optimize the routing path using hybrid particle swarm optimization with sequential variable neighbourhood search

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TELECOMMUNICATION SYSTEMS 2023年第2期84卷 153-165页

作者： Gopalan, S. Harihara Ashok, J. Manikandan, A. Ramalingam, S. Sri Ramakrishna Engn Coll Coimbatore India VSB Engn Coll Dept Elect & Commun Kaur India SSM Inst Engn & Technol Dept Elect & Commun Engn Dindugal 624002 India Sri Eshwar Coll Engn Dept Elect & Commun Engn Coimbatore 642109 India

A vehicular Ad-Hoc Network (VANET) is a form of Mobile Ad-Hoc Network (MANET) which employs wireless routers that are inside every vehicle to operate as a node. The process of data dissemination is used to improve the quality of travel to avoid unnecessary accidents in VANET. Many legacy protocols use this type of messaging activity to ensure fair road safety without concern for network congestion. Node congestion increases with control of routing overhead packets. Therefore, this paper proposes a Data Dissemination Protocol (DDP). VANET routing protocols can be divided into two categories: topology-based routing protocols and location-based routing protocols. The goal is to relay emergency signals to stationary nodes as soon as possible. The standard messages will be routed to the FIFO queue. Multiple routes were found using the Time delay-based Multipath Routing (TMR) approach to transmit these messages to a destination node, and particle swarm Optimisation (PSO) is utilized to find the optimal and secure path. Sequential Variable Neighborhood Search (SVNS) algorithm is applied in order to optimize the particles' position with Local Best particle and Global Best particle (LBGB). The proposed method PSO-SVNS-LBGB is compared with different methods such as PSO-SVNS-GB, PSO-SVNS-LB, PSO-SVNS-CLB, PSO-SVNS-CGB. The experimental results show significant improvements in throughput and packet loss ratio, reduced end-to-end delay, rounding overhead ratio, and energy consumption. The simulation environment was conducted in NS2.34 is preferred for network simulation, and the VANET simulator used is SUMO and MOVE software. With a 98.41 ms delay and an average speed of 60 km/h, the PSO-SVNS-LBGB approach is suggested.

关键词： Vehicular ad-hoc networks (VANETs) Bio-inspired routing Meta-heuristics particle swarm optimization algorithm Sequential variable neighbourhood search

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Regional Load Frequency Control of BP-PI Wind Power Generation Based on particle swarm optimization

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ENERGIES 2023年第4期16卷 2015-2015页

作者： Sun, Jikai Chen, Mingrui Kong, Linghe Hu, Zhijian Veerasamy, Veerapandiyan Qingdao Univ Coll Elect Engn Qingdao 266071 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

The large-scale integration of wind turbines (WTs) in renewable power generation induces power oscillations, leading to frequency aberration due to power unbalance. Hence, in this paper, a secondary frequency control strategy called load frequency control (LFC) for power systems with wind turbine participation is proposed. Specifically, a backpropagation (BP)-trained neural network-based PI control approach is adopted to optimize the conventional PI controller to achieve better adaptiveness. The proposed controller was developed to realize the timely adjustment of PI parameters during unforeseen changes in system operation, to ensure the mutual coordination among wind turbine control circuits. In the meantime, the improved particle swarm optimization (IPSO) algorithm is utilized to adjust the initial neuron weights of the neural network, which can effectively improve the convergence of optimization. The simulation results demonstrate that the proposed IPSO-BP-PI controller performed evidently better than the conventional PI controller in the case of random load disturbance, with a significant reduction to near 10 s in regulation time and a final stable error of less than 10(-3) for load frequency. Additionally, compared with the conventional PI controller counterpart, the frequency adjustment rate of the IPSO-BP-PI controller is significantly improved. Furthermore, it achieves higher control accuracy and robustness, demonstrating better integration of wind energy into traditional power systems.

关键词： wind power generation sudden load disturbance load frequency control BP neural network particle swarm optimization algorithm

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Global-guidance chaotic multi-objective particle swarm optimization method for pneumatic suspension handling and ride quality enhancement on the basis of a thermodynamic model of a full vehicle

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2023年第14期237卷 3334-3352页

作者： Ghorbany, Mohammad Ebrahimi-Nejad, Salman Mollajafari, Morteza Iran Univ Sci & Technol Sch Automot Engn Vehicle Dynam Syst Res Lab Tehran Iran Iran Univ Sci & Technol Sch Automot Engn Vehicle Elect & Elect Res Lab Tehran Iran Iran Univ Sci & Technol Sch Automot Engn Vehicle Elect & Elect Res Lab Farjam StHengam StResalat Sq Tehran *** Iran

In this paper, the parameters of the validated suspension system model of a full-vehicle are tuned through design sensitivity analyses, and then Multi-Objective particle swarm optimization (MOPSO) is used to enhance vehicle ride comfort, which is the vertical whole-body vibrations, and handling features, that is, roll motion and road holding, simultaneously. The parameters of this thermodynamic-based pneumatic suspension system model are comprised of the air spring reservoir volume, orifice resistance, initial volume, and pressure of the pneumatic springs. To enhance the convergence rate, computational times, and diversity of the swarm particles, we have incorporated chaotic dynamics into the MOPSO using the Logistic Map chaotic method to initialize the population and also employed the leader-based global guidance techniques to conduct the potential solutions in each iteration. The analysis of the proposed modeling and optimization results show that the suspension system has been reasonably boosted in terms of vehicle handling and ride comfort. Quantitatively, the RMS acceleration and pitch angle has been reduced by about 71% and 57%, respectively, showing a substantial improvement in passenger comfort. Furthermore, the proposed approach caused an increase in tire road-holding force by about 148% and a reduction of roll angle by 33% which results in an enhancement in vehicle handling, boosting vehicle driving safety.

关键词： Multi-objective optimization pneumatic springs particle swarm optimization algorithm optimal vehicle dynamics performance ride quality handling

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Hardware implementation of particle swarm optimization with chaotic fractional-order

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NEURAL COMPUTING & APPLICATIONS 2023年第15期35卷 11249-11268页

作者： Zermani, Aymen Manita, Ghaith Feki, Elyes Mami, Abdelkader Tunis El Manar Univ LAPER Dept Phys Fac Sci Campus Univ El Manar Tunis 2092 Tunisia Univ Sousse Lab MARS ISITCom LR17ES05 Sousse Tunisia

Engineering optimization methods based on meta-heuristic algorithms are computationally demanding and might present complex implementation issues for embedded devices with high constraints. One of the interesting meta-heuristic methods frequently utilized to address diverse optimization issues in the real world is particle swarm optimization (PSO). However, the most challenging constraints in real applications;having an optimal execution time for an optimal solution, added to avoid premature convergence and stagnation in the local optima. In this study, we propose the hardware implementation of an improved particle swarm optimization algorithm by using the fractional order concept and chaos theory named CF-PSO. The concept is to substitute a chaotic sequence generator for the values of random variables and to use fractional calculus in the velocity and position expressions. We provide a hardware design of the enhanced PSO on Field Programmable Gate Array (FPGA) to obtain substantially quicker execution speeds than those attainable in software implementation. A Xilinx Virtex-7 Pro Development Kit is used to implement the hardware-improved PSO design and evaluate its performance. To start, we put the suggested architecture to the test using a benchmark of four functions to demonstrate the effectiveness of this hardware implementation. Secondly, we contrasted it with three other PSO variations: the conventional PSO, the PSO using fractional order velocity (F-V-PSO), and the PSO using fractional order velocity and position (F-VP-PSO). The experimental results show that the hardware implementation of F-VP-PSO is between 1.082 and 21.25 times faster. CFPSO algorithm can be implemented in the Virtex7 VC707 evaluation platform by utilizing less than 2% of the slice 462 registers and 16% of the slice LUT (Look up Table) resources.

关键词： particle swarm optimization algorithm Fractional-order difference Chaos theory FPGA Hardware

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Parametric analysis and optimization of entropy generation in unsteady MHD flow over a stretching rotating disk using artificial neural network and particle swarm optimization algorithm

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ENERGY 2013年 55卷 497-510页

作者： Rashidi, M. M. Ali, M. Freidoonimehr, N. Nazari, F. Bu Ali Sina Univ Fac Engn Dept Mech Engn Hamadan Iran King Saud Univ Coll Engn Dept Mech Engn Riyadh 11421 Saudi Arabia Islamic Azad Univ Hamedan Branch Young Researchers & Elites Club Hamadan Iran

The present study first of all concerns the first and second law analyzes of an electrically conducting fluid past a rotating disk in the presence of a uniform vertical magnetic field, analytically via Homotopy Analysis Method (HAM), and then applies Artificial Neural Network (ANN) and particle swarm optimization (PSO) algorithm in order to minimize the entropy generation. In the first part of this study, entropy generation equation is derived as a function of velocity and temperature gradients and non-dimensionalized using geometrical and physical flow field-dependent parameters. A very good agreement can be seen between some of the obtained results of the current study and the results of the previously published data. The effects of physical flow parameters such as magnetic interaction parameter, unsteadiness parameter, disk stretching parameter, Prandtl number, Reynolds number and Brinkman number on all fluid velocity components, temperature distribution and the averaged entropy generation number are checked and analyzed. For minimizing the entropy generation value a procedure based on ANN and PSO is proposed. This procedure comprises three steps. The first step is to find entropy generation for values of some different affecting factors. In the second step, some distinct multi-layer perceptron ANNs based on the data obtained from step one are trained. In step three, PSO is used to minimize the entropy generation in the considered stretchable rotating disk. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： MHD flow Entropy generation Unsteady flow Rotating disk particle swarm optimization algorithm Artificial neural network

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A new particle swarm optimization-enhanced deep neural network for automatic ECG arrhythmias classification

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 2025年 1-15页

作者： Kahlessenane, Yaaqoub Bouaziz, Fatiha Siarry, Patrick Jijel Univ Elect Dept BP 98 Ouled Aissa Jije 18000 Algeria Jijel Univ Mechatron Lab LMT BP 98 Ouled Aissa Jijel 18000 Algeria Paris East Creteil Univ Lab Images Signals & Intelligent Syst LISSI Paris France

This study proposes an ECG classification system using particle swarm optimization (PSO) for automated deep neural network hyperparameter tuning. PSO optimizes five key parameters: neuron counts in two fully connected layers, dropout rate, learning rate, and optimizer selection. ECG signals undergo wavelet decomposition for feature extraction, with classification performed on the MIT-BIH Arrhythmia Database across five heartbeat classes. The PSO-optimized model achieves superior performance with 99.76% accuracy, 99.34% precision, and 99.21% F1 score, demonstrating PSO's effectiveness in improving model reliability while reducing manual effort.

关键词： Classification approach deep neural network particle swarm optimization algorithm feature extraction hyperparameters

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Parameters optimization of hybrid strategy recommendation based on particle swarm algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 168卷 114388-114388页

作者： Cai, Biao Zhu, Xinping Qin, Yangxin Chengdu Univ Technol Coll Informat Sci & Technol Chengdu 610059 Peoples R China

With the unprecedented development in the internet technology, the information overload issues have become more and more complex, resulting in users being unable to obtain the target information accurately and effectively in selecting the required information from a large pool of surfed data. In view of this a recommendation system can be used to predict the user's selection probability for different potential objects as an important tool, which can help to solve the information overload issues. So far, many personalized recommendation algorithms based on bipartite graphS have been proposed, most of which are based on the similarity degree among users or items, such as collaborative filtering (CF), mass diffusion (MD) and heat conduction (HC). Among many recommendation algorithms, the performances of algorithms are varied. MD algorithm has high recommendation accuracy but poor diversity, while HC algorithm has good diversity but low accuracy. In order to solve the dilemma in accuracy and diversity, some hybrid recommendation algorithm have been proposed. This paper has mainly focused on the hybrid recommendation algorithm HHM, and pointed out its shortcomings. Based on the reconsideration of the effect of item popularity in the recommendation process, an improved hybrid recommendation algorithm using dual parameter called IHM was proposed. The particle swarm optimization (PSO) algorithm was applied to the parameter optimal of the hybrid recommendation algorithm to obtain the parameters of the algorithm. Experiments on 3 real datasets indicated that the IHM algorithm is better than HHM algorithms in terms of the recommendation accuracy, diversity and novelty. Meanwhile, the IHM algorithm can also improve the recommendation for items with lower popularity and solve the cold start problem.

关键词： Recommendation system Personalized recommendation Information overload Hybrid recommendation algorithm particle swarm optimization algorithm

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Underwater remotely operated vehicle control system with optimized PID based on improved particle swarm optimization

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DESALINATION AND WATER TREATMENT 2023年 314卷 322-329页

作者： Liu, Weixing Xia, Zihan Wu, Linyan Guo, Guangkuo Zhu, Cheng Zhang, Zhiyang Cui, Lin Jiangsu Ocean Univ Sc Ocean Engn Lianyungang 222005 Peoples R China Jiangsu Ocean Univ Makarov Coll Marine Engn Lianyungang 222005 Peoples R China Minist Ind & Informat Technol Res Inst Elect 5 Guangzhou 510000 Peoples R China Harbin Engn Univ Sci & Technol Underwater Vehicle Lab Harbin 150001 Peoples R China Nat Ocean Technol Ctr Tianjin 300112 Peoples R China

On account of the exponential development of control theory and artificial intelligence technology in recent years, more and more control theory is applied to the control of robots, but PID control method due to the simple structure and good stability still has research value, the core and the difficulty is the optimization of PID parameters. In order to solve the underwater remotely operated vehicle (ROV) with high nonlinearity and strong coupling characteristics, it can adjust its attitude in time to ensure its control performance in the face of the disturbance of the external complex environment. By improving the particle swarm inertia weights, the particle swarm optimization (PSO) algorithm reduces the situation of falling into the local optimal solution and applies it to the PID adaptive parameterization. Comparing the improved PSO-PID with the traditional PID simulation, it is concluded that the improved PSO-PID control has certain improvement on the control performance of the ROV, which has certain feasibility.

关键词： Remotely operated vehicle PID control particle swarm optimization algorithm

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Automatic boiling water reactor control rod pattern design using particle swarm optimization algorithm and local search

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NUCLEAR ENGINEERING AND DESIGN 2013年 255卷 273-279页

作者： Wang, Cheng-Der Lin, Chaung Inst Nucl Energy Res Nucl Engn Div Longtan Township 32546 Taoyuan County Taiwan Natl Tsing Hua Univ Dept Engn & Syst Sci Hsinchu 30013 Taiwan

This study developed a method for the automatic design of a boiling water reactor (BWR) control rod pattern (CRP) using the particle swarm optimization (PSO) algorithm. The PSO algorithm is more random compared to the rank-based ant system (RAS) that was used to solve the same BWR CRP design problem in the previous work. In addition, the local search procedure was used to make improvements after PSO, by adding the single control rod (CR) effect. The design goal was to obtain the CRP so that the thermal limits and shutdown margin would satisfy the design requirement and the cycle length, which is implicitly controlled by the axial power distribution, would be acceptable. The results showed that the same acceptable CRP found in the previous work could be obtained. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Boiling water reactor control rod pattern particle swarm optimization algorithm Local search

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Path Planning of Continuum Robot Based on a New Improved particle swarm optimization algorithm

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Journal of Harbin Institute of Technology(New Series) 2013年第4期20卷 78-84页

作者： Fang Gao Qiang Zhao Gui-Xian Li School of Mechanical Electrical Engineering Harbin Institute of Technology Traffic College Northeast Forestry University

Continuum robot is a new type of biomimetic robot,which realizes the motion by bending some parts of its *** its path planning becomes more difficult even compared with hyper-redundant *** this paper a circular arc spline interpolating method is proposed for the robot shape description,and a new two-stage position-selectable-updating particle swarm optimization(TPPSO)algorithm is put forward to solve this path planning *** algorithm decomposes the standard PSO velocity’s single-step updating formula into twostage multi-point updating,specifically adopting three points as candidates and selecting the best one as the updated position in the first half stage,and similarly taking seven points as candidates and selecting the best one as the final position in the last half *** scheme refines and widens each particle’s searching trajectory,increases the updating speed of the individual best,and improves the converging speed and *** at the optimization objective to minimize the sum of all the motion displacements of every segmental points and all the axial stretching or contracting displacements of every segment,the TPPSO algorithm is used to solve the path planning *** detailed solution procedure is *** examples of five path planning cases show that the proposed algorithm is simple,robust,and efficient.

关键词： continuum robot path planning particle swarm optimization algorithm

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