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Underwater Acoustic Sensor Networks: An Energy Efficient and Void Avoidance Routing Based on Grey Wolf optimization algorithm

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021年第4期46卷 3939-3954页

作者： Gola, Kamal Kumar Gupta, Bhumika Teerthanker Mahaveer Univ Fac Engn Dept Comp Sci & Engn Moradabad 244001 India Govind Ballabh Pant Inst Engn & Technol Dept Comp Sci & Engn Pauri Garhwal 246194 Uttarakhand India

The purpose of underwater acoustic sensor networks (UWASNs) is to find varied applications for ocean monitoring and exploration of offshore. In majority of these applications, the network comprises of several sensor nodes deployed at different depths in water. The sensor nodes which are situated in depth, at the sea bed, are unable to communicate unswervingly with those nodes which are close to the surface level;these nodes necessitate multi-hop communication which is facilitated by suitable routing plan. The working of UWASNs is affected by some constraints like high transmission delay, energy consumption, deployment, long propagation delay and high attenuation. Apart from this, the existence of void region in the route can also affect the overall performance of UWASNs. So, the void region can be avoided by considering the best forwarder node. The selection of the best forwarder node depends on depth variance, depth difference, residual energy, and link quality. Apart from this, an angle is also considered to select the best forwarder node. This paper presents an energy efficient and void region avoidance routing. The concept of grey wolf optimization algorithm is used here to select the best forwarder node. The proposed work increases the network lifetime by avoiding the void region and also balancing the network energy. The proposed work is simulated in the MATLAB platform and compared with weighting depth and forwarding area division depth-based routing and energy and depth variance-based opportunistic void avoidance schemes. This work achieves the packet delivery ratio 96% with varying transmission range up to 1000 m at 180 node size. Along with this, it decreases the end-to-end delay and average number of dead nodes up to 53% and 145, respectively. This work also improves the overall network lifetime and reduces the transmission delay. This work also propagates 55% less copies of data packets. Similar to this, some other performance metrics are also explained

关键词： Void avoidance optimization algorithm Delay Energy efficient Network lifetime

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QoS-Aware Cloud Service optimization algorithm in Cloud Manufacturing Environment

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Intelligent Automation & Soft Computing 2023年第8期37卷 1499-1512页

作者： Wenlong Ma Youhong Xu Jianwei Zheng Sadaqat ur Rehman School of Information Engineering Quzhou College of TechnologyQuzhou324000China School of Computer Science and Technology Zhejiang University of TechnologyHangzhou310023China Department of Natural and Computing Science University of AberdeenScotlandAberdeenAB243FXUK

In a cloud manufacturing environment with abundant functionally equivalent cloud services,users naturally desire the highest-quality service(s).Thus,a comprehensive measurement of quality of service(QoS)is ***-mizing the plethora of cloud services has thus become a top *** ser-vice optimization is negatively affected by untrusted QoS data,which are inevitably provided by some *** resolve these problems,this paper proposes a QoS-aware cloud service optimization model and establishes QoS-information awareness and quantiﬁcation *** data are assessed by an information correction *** weights discovered by the variable precision Rough Set,which mined the evaluation indicators from historical data,providing a comprehensive performance ranking of service *** manufacturing cloud service optimization algorithm thus provides a quantitative reference for service *** experimental simulations,this method recommended the optimal services that met users’needs,and effectively reduced the impact of dis-honest users on the selection results.

关键词： Cloud manufacturing quality of service optimization algorithm rough set

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Study of a New Global optimization algorithm Based on the Standard PSO

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JOURNAL OF optimization THEORY AND APPLICATIONS 2013年第3期158卷 935-944页

作者： Yang, B. Cheng, L. Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China

In this paper, a new global optimization algorithm is developed, which is named Particle Swarm optimization combined with Particle Generator (PSO-PG). Based on a series of comparable numerical experiments, we show that the calculation accuracy of the new algorithm is greatly improved and optimization efficiency is increased as well, in comparison with those of the standard PSO. It is also found that the optimization results obtained from PSO-PG are almost independent of the coefficients adopted in the algorithm.

关键词： Particle swarm optimization (PSO) Particle generator optimization algorithm Global optimization

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A New Method for Determining Coal Seam Permeability based on a Gas-Solid Coupling Model and PSO plus LM Hybrid optimization algorithm

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NATURAL RESOURCES RESEARCH 2023年第3期32卷 1265-1282页

作者： Liu, Quanlin Li, Zhonghui Wang, Enyuan Kong, Xiangguo Feng, Xiaojun Liu, Yubing Wang, Dongming Zhang, Chaolin China Univ Min & Technol Key Lab Gas & Fire Control Coal Mines Minist Educ Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol State Key Lab Coal Resources & Safe Min Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Safety Engn Xuzhou 221116 Jiangsu Peoples R China Xian Univ Sci & Technol Coll Safety Sci & Engn Xian 710054 Shaanxi Peoples R China

Previous underground test methods for coal seam permeability are usually based on radial flow theory, which ignores the impact of coal deformation and permeability dynamic evolution. Thus, these methods have some limitations in theoretical reliability, result stability and method applicability. Therefore, this paper derives a gas-solid coupling model considering pore sorption strain. Based on this model and a hybrid optimization algorithm, which combines particle swarm optimization (PSO) and Levenberg-Marquardt (LM) algorithm, a novel method for determining coal seam permeability, namely the GP method, is proposed. The feasibility and reliability of this method were verified by numerical experiments and field tests, respectively. The findings indicate that the proposed PSO + LM algorithm was superior to PSO algorithm and LM algorithm in terms of convergence and computational efficiency. In field application, the test value of the GP method was closer to the true value of coal seam permeability, and its theoretical model can better reflect the change of borehole gas flow, whether compared with the traditional radial flow method from the global perspective or compared with other classical permeability models from the local perspective. Therefore, the GP method has the potential to become an effective test method for coal seam permeability. This study has certain reference significance for the acquisition of mechanical parameters and gas parameters.

关键词： Coal seam permeability Underground test Coupling model optimization algorithm

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Synchronous Inversion of Coal Seam Gas Pressure and Permeability Based on a Dual Porosity/Dual Permeability Model and Surrogate optimization algorithm

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NATURAL RESOURCES RESEARCH 2023年第5期32卷 2115-2136页

作者： Liu, Quanlin Li, Zhonghui Wang, Enyuan Feng, Xiaojun Kong, Xiangguo Wang, Dongming China Univ Min & Technol Key Lab Gas & Fire Control Coal Mines Minist Educ Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol State Key Lab Coal Resources & Safe Min Xuzhou 221116 Jiangsu Peoples R China China Univ Min & Technol Sch Safety Engn Xuzhou 221116 Jiangsu Peoples R China Xian Univ Sci & Technol Coll Safety Sci & Engn Xian 710054 Shanxi Peoples R China

Rapid and accurate acquisition of permeability and gas pressure is crucial for gas development and disaster prevention in coal mines. However, these two parameters are currently challenging to obtain simultaneously using traditional testing methods. Moreover, due to the theoretical basis of the radial flow equation, most methods are only applicable to cross-seam borehole, which brings great limitations to the application of the methods. Therefore, this paper derives a new dual porosity/dual permeability model considering the impacts of time-dependent Fick diffusion, matrix mechanical and sorption strain. According to this coupling model and the surrogate optimization (SO) algorithm, a synchronous inversion method of the gas pressure and permeability is suggested. Then, the dependability of this method is validated by field tests. The findings demonstrate that, in contrast to other theoretical models and optimization algorithms, the new model can more precisely depict the actual change of borehole gas flow, and the SO algorithm can find a global optimal solution with higher accuracy in less time. Compared with traditional methods, the new method has the advantages of strong universality, short testing cycle and high automatic level. Therefore, this method has the potential to be an effective tool for obtaining coal seam gas parameters.

关键词： Gas pressure Permeability Coal seam Synchronous inversion Coupling model optimization algorithm

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A Reliability optimization algorithm for Wireless Sensor Network

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INTERNATIONAL JOURNAL OF ONLINE ENGINEERING 2018年第6期14卷 138-150页

作者： Zhang, Qiuming Luo, Jing China Univ Geosci Wuhan Hubei Peoples R China Wuhan Vocat Coll Software & Engn Practice Training Ctr Wuhan Hubei Peoples R China

Aiming at the reliability optimization algorithm based on wireless sensor network, a data fusion algorithm based on extreme learning machine for wireless sensor network was proposed according to the temporal spatial correlation in data collection process. After analyzing the principles, design ideas and implementation steps of extreme learning machine algorithm, the performance and results were compared with traditional BP algorithm, LEACH algorithm and RBF algorithm in simulation environment. The simulation results showed that the data fusion optimization algorithm based on the limit learning machine for wireless sensor network was reliable. It improved the efficiency of fusion and the comprehensive reliability of the network. Thus, it can prolong the life cycle and reduce the total energy consumption of the network.

关键词： wireless sensor network optimization algorithm extreme learning machine

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CHP Economic Dispatch Considering Prohibited Zones to Sustainable Energy Using Self-Regulating Particle Swarm optimization algorithm

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2020年第3期44卷 1147-1164页

作者： Ara, Afshin Lashkar Shahi, Nastaran Mohammad Nasir, Mohammad Islamic Azad Univ Mat & Energy Res Ctr Dezful Branch POB 313 Dezful Iran

Economic dispatch is the optimal scheduling for generating units with technical constraints. Combined heat and power economic dispatch (CHPED) refers to minimization of the total energy cost for generating electricity and heat supply to load demand. This planning model integrates heat and power energy to balance energy supply and demand, mitigate climate change and improve energy efficiency of sustainable cities and green buildings. In this paper for the first time, self-regulating particle swarm optimization (SRPSO) algorithm is utilized for solving the CHPED problem by considering valve point effects and prohibited zones on fuel cost function of pure generation units and electrical power losses in transmission systems. The main advantage of SRPSO algorithm to PSO algorithm is the inertia weight flexibility with respect to search conditions. In this algorithm, unlike PSO algorithm that inertia weight reduces in each iteration, this value increases or reduces proportional to particles' positions, which will lead particles to achieve optimal value with higher speed. The capability and effectiveness of the proposed algorithm are evaluated on a large-scale energy system using MATLAB environment. The results obtained by SRPSO algorithm are outperformed by other optimization methods from the economic, sustainable energy and time consumption point of view.

关键词： Combined heat and power (CHP) optimization algorithm Cogeneration units Economic dispatch (ED) Self-regulating particle swarm optimization

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Predicting density log from well log using machine learning techniques and heuristic optimization algorithm:A comparative study

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Petroleum Research 2024年第2期9卷 176-192页

作者： Mehdi Rahmati Ghasem Zargar Abbas Ayatizadeh Tanha National Iranian Drilling Company Well Logging DepartmentAhwazIran Faculty Member of Petroleum University of Technology Iran Department of Petroleum Engineering Tarbiat Modares UniversityTehranIran

In the petroleum industry,the analysis of petrophysical parameters is critical for efficient reservoir management,production optimization,development strategies,and accurate hydrocarbon reserve *** recent years,the integration of machine learning methodologies has revolutionized the field,addressing challenges in geology,geophysics,and petroleum engineering,even when confronted with limited or imperfect *** study focuses on the prediction of density logs,a pivotal factor in evaluating reservoir hydrocarbon *** is important to note that during well logging operations,log data for specific depths of interest may be missing or incorrect,presenting a significant *** tackle this issue,we employed the Adaptive Neuro-Fuzzy Inference System(ANFIS)and Artificial Neural Networks(ANN)in combination with advanced optimization algorithms,including Particle Swarm optimization(PSO),Imperialist Competitive algorithms(ICA),and Genetic algorithms(GA).These methods exhibit promising performance in predicting density logs from gamma-ray,neutron,sonic,and photoelectric log ***,our results highlight that the Genetic algorithms-based Artificial Neural Network(GA-ANN)approach outperforms all other methods,achieving an impressive Mean Squared Error(MSE)of *** comparison,ANFIS records an MSE of 0.0015,ICA-ANN 0.0090,PSO-ANN 0.0093,and ANN 0.0183.

关键词： Density log Machine learning approaches Artificial neural networks(ANN) Adaptive neuro-fuzzy inference system(ANFIS) optimization algorithm

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Integration of fruit fly and firefly optimization algorithm with support vector regression in estimating daily pan evaporation

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INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2024年第2期68卷 237-251页

作者： Sharafi, Milad Samadianfard, Saeed Behmanesh, Javad Prasad, Ramendra Urmia Univ Dept Water Engn Orumiyeh Iran Univ Tabriz Fac Agr Dept Water Engn Tabriz Iran Univ Fiji Sch Sci & Technol Dept Sci Lautoka Fiji

The purpose of the present study was to predict the pan evaporation values at four stations including Urmia, Makou, Mahabad, and Khoy, located in West Azerbaijan, Iran, using support vector regression (SVR), SVR coupled by fruit fly algorithm (SVR-FOA), and SVR coupled with firefly algorithm (SVR-FFA). Therefore, for the first time, this research has used the combined SVR-FOA to predict pan evaporation. For this purpose, meteorological parameters in the period of 1990-2020 were gathered and then using the Pearson's correlation coefficient, significant inputs for pan evaporation estimation were determined. The correlation evaluation of the parameters showed that the two parameters of wind speed and sunshine hours had the highest correlation with the pan evaporation values, and in addition, these parameters, as input to the models, improved the results and increased the accuracy of the models. The obtained results indicated that at Urmia station, SVR-FFA with the lowest error was the best model. The SVR-FOA also had better performance than the SVR model. Additionally, the result showed that SVR-FOA with the lowest errors had the best capability in pan evaporation estimation at other studied stations. Therefore, it was concluded that FOA with advantages such as simplicity, fewer parameters, easy adjustment, and less calculation can significantly increase the capability of independent SVR models. Hence, based on the overall results, SVR-FOA may be recommended as the most accurate method for pan evaporation estimation.

关键词： Evaporation optimization algorithm Statistical analysis Water resource management

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Research on neural network optimization algorithm for building energy consumption prediction

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2018年第3期18卷 695-707页

作者： Chen, Song Ren, Ting-Ting Wu, Zhong-Cheng Chinese Acad Sci High Field Magnet Lab Hefei Anhui Peoples R China Univ Sci & Technol China Hefei Anhui Peoples R China Anhui Jianzhu Univ Coll Mech & Elect Engn Hefei Anhui Peoples R China

Building energy consumption prediction per month is an important content of building energy consumption management and company's financial budget. BP neural network with parameter optimization, network optimized by mind evolutionary algorithm, network optimized by genetic algorithm, network optimized by particle swarm algorithm and network optimized by adaptive weight particle swarm algorithm are used to forecast the energy consumption. The optimal values of the learning rate and hidden layer node number are choosen. The characteristics of various kinds of optimization algorithm are compared. The neural network optimized by adaptive weight particle swarm algorithm is proved to be the most accurate in predicting energy consumption.

关键词： BP neural network optimization algorithm energy consumption prediction

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