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Data collection optimization of ocean observation network based on AUV path planning and communication

OCEAN ENGINEERING 2023年第1期282卷

作者： Yan, Z. Li, Y. Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China Natl Univ Singapore Coll Design & Engn Singapore 119077 Singapore Technol Ctr Offshore & Marine Singapore TCOMS Singapore 118411 Singapore

The Ocean Observation Network (OON) is an ocean science and engineering observation platform which integrates underwater wireless sensors, acoustic buoys, submersible buoys, and sonars. The complex underwater environment makes it challenging to place the observation nodes flexibly. In this research, firstly, a novel OON data collection optimization scheme is proposed, which is based on Autonomous Underwater Vehicle (AUV) path planning and communication. By combining the Voronoi diagram and K-means algorithm, an optimized clustering algorithm (VorK-means) is proposed to optimize the location of junction boxes and reduce the losses in the transmission energy of submarine cables in the OON simultaneously. Subsequently, a computational algorithm for AUV data collection position is proposed based on Voronoi vertices and communication circles. Moreover, an improved grey wolf optimization (GWO) algorithm, based on the Levy flight and dimension (LD) search strategy, is proposed and applied for the AUV path planning, which is named LD-GWO. The accuracy and efficiency are validated by applying the proposed algorithms to the Canadian North-East Pacific Time-series Underwater Networked Experiment (NEPTUNE). Compared with the K-means algorithm, the VorKmeans algorithm can reduce the total length of submarine cables by nearly 14%, and compared with the GWO algorithm, LD-GWO can obtain shorter data collection paths under identical parameter settings.

关键词： Ocean observatory networks grey wolf optimization algorithm Path planning and communication

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Hybrid algorithms based on combining reinforcement learning and metaheuristic methods to solve global optimization problems

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KNOWLEDGE-BASED SYSTEMS 2021年 223卷 107044-107044页

作者： Seyyedabbasi, Amir Aliyev, Royal Kiani, Farzad Gulle, Murat Ugur Basyildiz, Hasan Shah, Mohammed Ahmed Beykent Univ Fac Engn & Architecture Dept Comp Engn Istanbul Turkey Istanbul Aydin Univ Fac Engn Dept Comp Engn Istanbul Turkey Istanbul Arel Univ Fac Engn & Architecture Dept Comp Engn Istanbul Turkey Istanbul Ayvansaray Univ Fac Engn Dept Comp Engn Istanbul Turkey Istinye Univ Fac Engn Software Engn Dept Istanbul Turkey

This paper introduces three hybrid algorithms that help in solving global optimization problems using reinforcement learning along with metaheuristic methods. Using the algorithms presented, the search agents try to find a global optimum avoiding the local optima trap. Compared to the classical metaheuristic approaches, the proposed algorithms display higher success in finding new areas as well as exhibiting a more balanced performance while in the exploration and exploitation phases. The algorithms employ reinforcement agents to select an environment based on predefined actions and tasks. A reward and penalty system is used by the agents to discover the environment, done dynamically without following a predetermined model or method. The study makes use of Q-Learning method in all three metaheuristic algorithms, so-called RLI-GWO, RLEx-GWO, and RLWOA algorithms, so as to check and control exploration and exploitation with Q-Table. The Q-Table values guide the search agents of the metaheuristic algorithms to select between the exploration and exploitation phases. A control mechanism is used to get the reward and penalty values for each action. The algorithms presented in this paper are simulated over 30 benchmark functions from CEC 2014, 2015 and the results obtained are compared with well-known metaheuristic and hybrid algorithms (GWO, RLGWO, I-GWO, Ex-GWO, and WOA). The proposed methods have also been applied to the inverse kinematics of the robot arms problem. The results of the used algorithms demonstrate that RLWOA provides better solutions for relevant problems. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Metaheuristic algorithm Reinforcement learning algorithm grey wolf optimization algorithm Whale optimization algorithm Q-learning

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An Optimized Self-Compensated Solution for Temperature and Strain Cross-Sensitivity in FBG Interrogators Based on Edge Filter

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SENSORS 2021年第17期21卷 5828-5828页

作者： Silveira, Mariana L. Rocha, Helder R. O. Antunes, Paulo F. C. Andre, Paulo S. B. Segatto, Marcelo E., V Frizera, Anselmo Diaz, Camilo A. R. Univ Fed Espirito Santo Elect Engn Dept Telecommun Lab LABTEL BR-29075910 Vitoria ES Brazil Univ Aveiro I3N P-3810193 Aveiro Portugal Univ Aveiro Phys Dept P-3810193 Aveiro Portugal Inst Telecomunicacoes Campus Univ Santiago P-3810193 Aveiro Portugal Univ Lisbon Inst Telecomunicacoes Inst Super Tecn Dept Elect & Comp Engn P-1049001 Lisbon Portugal

Optical fiber sensors based on fiber Bragg gratings (FBGs) are prone to measurement errors if the cross-sensitivity between temperature and strain is not properly considered. This paper describes a self-compensated technique for canceling the undesired influence of temperature in strain measurement. An edge-filter-based interrogator is proposed and the central peaks of two FBGs (sensor and reference) are matched with the positive and negative slopes of a Fabry-Perot interferometer that acts as an optical filter. A tuning process performed by the grey wolf optimizer (GWO) algorithm is required to determine the optimal spectral characteristics of each FBG. The interrogation range is not compromised by the proposed technique, being determined by the spectral characteristics of the optical filter in accordance with the traditional edge-filtering interrogation. Simulations show that, by employing FBGs with optimal characteristics, temperature variations of 30 degrees C led to an average relative error of 3.4% for strain measurements up to 700 mu epsilon. The proposed technique was experimentally tested under non-ideal conditions: two FBGs with spectral characteristics different from the optimized results were used. The temperature sensibility decreased by 50.8% as compared to a temperature uncompensated interrogation system based on an edge filter. The non-ideal experimental conditions were simulated and the maximum error between theoretical and experimental data was 5.79%, proving that the results from simulation and experimentation are compatible.

关键词： fiber optical sensors fiber Bragg gratings edge filter temperature cross-sensitivity grey wolf optimization algorithm

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Multi-Objective Optimal Control Method for the 6-DOF Robotic Crusher

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APPLIED SCIENCES-BASEL 2024年第20期14卷 9397页

作者： Duan, Guochen Yao, Lele Zhan, Zhanyu Kang, Tao Guo, Chaoyue China North Vehicle Res Inst Beijing 100072 Peoples R China

In order to achieve the best crushing effect of the 6-DOF robotic crusher, a multi-objective optimal control method for the 6-DOF robotic crusher has been proposed. Taking the mass fraction of crushed products below 12 mm, total energy consumption, effective energy consumption, output, and wear as the working indexes, and taking the suspension point, precession angle, and swing frequency of the mantle as the working conditions of the crusher, the working indexes under different working conditions are calculated. And, based on the above parameters, the optimization objective function of the 6-DOF robotic crusher is obtained. The weight determination method of fuzzy multiple attributes decision making (FMADM) is used to determine the equivalent wear and the optimization target weight. Compared with the original scheme, the output increases and the energy consumption decreases significantly. The results can be used as a reference for the control strategy of the 6-DOF robotic crusher. It can also be used as a reference for the design of a traditional cone crusher.

关键词： 6-DOF robotic crusher multi-objective optimization fuzzy multiple attributes decision making (FMADM) grey wolf optimization algorithm

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Adaptive real-time ECMS with equivalent factor optimization for plug-in hybrid electric buses

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ENERGY 2024年 304卷

作者： Sun, Xiaodong Chen, Zongzhe Han, Shouyi Tian, Xiang Jin, Zhijia Cao, Yunfei Xue, Mingzhou Jiangsu Univ Automot Engn Res Inst Zhenjiang 212013 Peoples R China Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Peoples R China

The plug-in hybrid electric bus (PHEB) is one of the vehicles that can address global environmental and energy problems. Due to the complex characteristics of the urban cycle condition, a fixed parameter energy management strategy may not be suitable for fuel economy. Thus, an adaptive real-time control strategy seems to be of great significance for PHEBs. The paper designs an advanced equivalent consumption minimization strategy (ECMS) with segments divided from the driving cycle and optimized equivalent factor (EF). The classification of different segments is based on the time that the vehicle stops. The EF is optimized under two different driving conditions by using the grey wolf optimization (GWO) algorithm. A new model, as the difference between the actual state of charge (SOC) and the reference SOC, is presented and minimized by optimizing the EF. Segmented EF optimization adds a correction factor, which can adjust EF according to the kinematic characteristics of each segment. The results show that the fuel consumption decreased by 18.72 % compared with the conventional ECMS.

关键词： Energy management strategy Equivalent consumption minimization strategy (ECMS) Plug-in hybrid electric bus (PHEB) grey wolf optimization algorithm

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Research on Two-Stage Semi-Active ISD Suspension Based on Improved Fuzzy Neural Network PID Control

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SENSORS 2023年第20期23卷 8388页

作者： Jin, Linhao Fan, Jingjing Du, Fu Zhan, Ming North China Univ Technol Sch Elect & Control Engn Beijing 100144 Peoples R China Beijing Inst Technol Sch Mech Vehicular Engn Beijing 100081 Peoples R China

To better improve the ride comfort and handling stability of vehicles, a new two-stage ISD semi-active suspension structure is designed, which consists of the three elements, including an adjustable damper, spring, and inerter. Meanwhile, a new semi-active ISD suspension control strategy is proposed based on this structure. Firstly, the fuzzy neural network's initial parameters are optimized using the grey wolf optimization algorithm. Then, the fuzzy neural network with the optimal parameters is adjusted to the PID parameters. Finally, a 1/4 2-degree-of-freedom ISD semi-active suspension model is constructed in Matlab/Simulink, and the dynamics simulation is carried out for the three schemes using PID control, fuzzy neural network PID control, and improved fuzzy neural network PID control, respectively. The results show that compared with adopting PID control and fuzzy neural network PID control strategy, the vehicle body acceleration and tire dynamic loads are significantly reduced after using the grey wolf optimized fuzzy neural network PID control strategy, which shows that the control strategy proposed in this paper can significantly improve the vehicle smoothness and the stability of the handling.

关键词： two-stage ISD semi-active suspension grey wolf optimization algorithm fuzzy neural network Matlab/Simulink

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A Guided Mutation grey wolf Optimizer algorithm Integrating Flower Pollination Mechanism

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电脑学刊 2022年第2期33卷 51-67页

作者： Jing-Sen Liu Qing-Qing Liu Fang Zuo

The basic grey wolf Optimizer (GWO) has some shortcomings, for example, the convergence speed is slow, it is easy to fall into local extremum, and high-dimensional optimization ability is poor and so on. In response to these shortcomings, an improved grey wolf algorithm which combines flower pollination mechanism, teaching mechanism and polynomial variation is proposed in this study. The flower pollination mechanism is integrated with GWO algorithm, Levy distribution is introduced into the global search of grey wolf population. And the double random mechanism is added in the local search, for these improvements, this algorithm's overall optimization performance is improved. The teaching mechanism is added to α wolf to improve the algorithm's convergence speed. Polynomial mutation is applied to the individuals with poor optimization effect to improve the algorithm's accuracy and its ability to jump out of local extremum. Theoretical analysis shows that the time complexity of the improved algorithm is the same as that of the basic algorithm. The test results of five representative comparison algorithms on multiple different characteristics and different dimensions of CEC2017 benchmark functions and two classical engineering problems show that FMGWO algorithm has high optimization accuracy, convergence speed and solution stability. Therefore, it has obvious advantages in global optimization.

关键词： grey wolf optimization algorithm flower pollination algorithm teaching mechanism polynomial mutation CEC2017

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An Improved Approach for Load Balancing among Virtual Machines in Cloud Environment

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Procedia Computer Science 2022年 215卷 556-566页

作者： Suman Sansanwal Nitin Jain Research Scholar Chandigarh University Mohali 140413 India Professor Chandigarh University Mohali 140413 India

Cloud computing has shown noteworthy evolution in information technology. Users can enjoy various services of cloud technology only if internet connection is available. In cloud computing, load balancing considered as a fundamental issue that has confronted researchers in this domain. Load balancing basically works by allotting fair and efficient work among computing resources which ultimately achieve high user satisfaction and raises systems productivity. Many load-balancing techniques made efforts to resolve this problem using metaheuristics algorithm, and amplify the operation and efficiency of systems. In this paper, existing load balancing techniques has been discussed and research gaps in existing lirture hasbeeb discussed. Also a new technique has been proposed called IG-GWO - Inquisitive Genetic grey wolf optimization algorithm using combination of grey wolf optimization (GWO) algorithm and Genetic algorithm.

关键词： Cloud computing load balancing grey wolf optimization algorithm Cloudsim

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CONSTRUCTION AND APPLICATION OF A BEARING FAULT DIAGNOSIS MODEL BASED ON IMPROVED GWO algorithm

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Diagnostyka 2024年第3期25卷

作者： Jiang, Lingbo School of Mechanical and Electronic Engineering Shandong Vocational College of Industry Zibo256414 China

In mechanical equipment, if bearing components fail, it can cause serious equipment damage and even threaten human life safety. Therefore, equipment bearings fault diagnosis is of great meaning. In the study of bearing fault diagnosis, an improved gray wolf optimization algorithm is put forward to optimize the support vector machine model. The model improves the convergence factor of the algorithm, and then optimizes the penalty factor and KF parameters of the support vector machine to enhance the accuracy of fault classification. At the same time, in the problem of fault identification, the introduction of adaptive noise set empirical mode decomposition and the combination of permutation entropy are studied to minimize the impact of noise on the identification model. The experimental outcomes indicated that the algorithm proposed in the study had an average fitness value and a standard deviation fitness value of 0 in the benchmark test function and 94.55% accuracy in overall fault identification. The permutation entropy of the vibration signal in the normal state of the bearing was within the range of [0.13, 0.52], which has a more stable state compared to the fault state. The results show that the improved grey wolf optimization algorithm is applied to the optimization of support vector machine, combined with the adaptive noise set empirical mode decomposition and permutation entropy, and the identification and classification results of bearing faults are successfully improved, making the proposed method feasible in bearing fault diagnosis, and providing a more effective scheme for fault diagnosis. © 2024 by the Authors. Licensee Polish Society of Technical Diagnostics (Warsaw. Poland).

关键词： bearings fault grey wolf optimization algorithm permutation entropy support vector machine

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Research on transformer fault diagnosis method based on ACGAN and CGWO-LSSVM

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SCIENTIFIC REPORTS 2024年第1期14卷 1-17页

作者： Guan, Shan Wu, Tong-yu Yang, Hai-qi Northeast Elect Power Univ Sch Mech Engn Jilin 132012 Peoples R China

This paper proposes a transformer fault diagnosis method based on ACGAN and CGWO-LSSVM to address the problem of misjudgment and low diagnostic accuracy caused by the small number and uneven distribution of some fault samples in transformer fault diagnosis. Firstly, generate adversarial networks through auxiliary classification conditions, The ACGAN method expands a small and imbalanced number of samples to obtain balanced and expanded data;Secondly, the non coding ratio method is used to construct the characteristics of dissolved gases in oil, and kernel principal component analysis is used, KPCA method for feature fusion;Finally, using the improved cubic gray wolf optimization algorithm, CGWO for least square support vector machines, optimize the parameters of the LSSVM model and construct a transformer fault diagnosis model. The results show that the proposed method has a low false alarm rate and a diagnostic accuracy of 97.66%, compared to IGOA-LSSVM the IChOA-LSSVM and PSO-LSSVM methods improved accuracy by 0.12, 1.76, and 2.58%, respectively. This method has been proven to solve the problems of misjudgment and low diagnostic accuracy caused by small sample sizes and uneven distribution. It is suitable for multi classification fault diagnosis of transformer imbalanced datasets and is superior to other methods.

关键词： Transformer fault diagnosis Unbalanced small samples ACGAN grey wolf optimization algorithm Least squares support vector machine

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