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Upper bound solution for active thrust of nonhomogeneous cohesive soil considering effects of intermediate principal stress

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Journal of Central South University 2022年第2期29卷 582-595页

作者： TANG Ren-hua MAO Feng-shan CHEN Chang-fu LEI Ming College of Civil Engineering Changsha UniversityChangsha 410022China Key Laboratory of Building Safety and Energy Efficiency of the Ministry of Education Hunan UniversityChangsha 410082China College of Civil Engineering Hunan UniversityChangsha 410082China

Considering the variation of cohesion along the depth,the upper bound solution of active earth pressure for a rough inclined wall with sloped backfill is formulated based on a log-spiral failure *** a more accurate prediction,the influence of intermediate principal stress is taken into consideration using the unified strength *** the search for the active pressure to an optimization problem,the most critical failure surface can be located by a natural selection-based gravitational search algorithm(GSA).The proposed method is validated compared with existing methods for noncohesive and cohesive cases and proved to be more accordance with the limit equilibrium *** influences of the variation of soil cohesion and intermediate principal stress on active earth pressure coefficient are then fully *** can be concluded that both the variations of soil cohesion and intermediate principal stress have a significant influence on the active earth pressure coefficient.

关键词： unified strength theory earth pressure logarithmic spiral failure surface upper bound method gravitational search algorithm

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gravitational search algorithm Based Mobile Aggregator Sink Nodes for Energy Efficient Wireless Sensor Networks

Gravitational Search Algorithm Based Mobile Aggregator Sink ...

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International Conference on Circuits, Power and Computing Technologies

作者： J. Rejina Parvin C. Vasanthanayaki Assistant Professor Dept. of ECE RVS College of Engineering and Technology Coimbatore India. Associate Professor Dept. of ECE Govt. College of Technology Coimbatore India.

ISBN: (纸本)9781467349215

A novel method of data aggregation mechanism using mobile sink nodes in Wireless Sensor Network (WSN) is proposed to improve the lifetime of the sensor nodes. Usage of sink nodes in WSNs is in existence, to collect data from the Cluster Heads;those are located far from the Base Station. Such sink nodes, extend the lifetime of Cluster Heads by reducing the communication overload of it. In the proposed work, nodes are allowed to form as clusters and no Cluster Head election takes place. Instead, the role of Cluster Heads is taken over by the introduced mobile sink nodes to perform data gathering, data aggregation as well as communication operations. This results in increasing the lifetime of the sensor nodes. gravitational search algorithm (GSA) is an optimization algorithm which is used for cluster formation and routing the data to achieve higher throughput. Simulations are carried out using Network Simulator. For the proposed and existing techniques, performance is evaluated and analyzed in terms of energy consumption and network lifetime. The simulation results are compared and discussed for the both proposed and existing techniques.

关键词： Mobile aggregator sinks gravitational search algorithm data gathering and aggregation

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gravitational search algorithm: A Novel Optimization Approach for Economic Load Dispatch

Gravitational Search Algorithm: A Novel Optimization Approac...

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Annual International Conference on Emerging Research Areas

作者： Mugdha Udgir Hari Mohan Dubey Manjaree Pandit Dept. of Electrical Eng. Madhav Institute of Technology and Science

ISBN: (纸本)9781467351508

The application of gravitational search algorithm a heuristic optimization approach for solution of economic load dispatch is presented here. GSA optimization approach is inspired by Newtonian gravity and law of motion. The efficacy and robustness of the GSA approach is analyzed on four different standard test cases. Comparison of computational results is made with the recent available methods. The computational results confirm superiority, faster convergence and adeptness of the algorithm.

关键词： gravitational search algorithm Economic Load Dispatch (ELD) Ramp Rate Limit (RRL) and Prohibited Operating zones (POZ)

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Universe-inspired algorithms for control engineering: A review

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HELIYON 2024年第11期10卷 e31771页

作者： Bernardo, Rodrigo M. C. Torres, Delfim F. M. Herdeiro, Carlos A. R. dos Santos, Marco P. Soares Univ Aveiro Ctr Mech Technol & Automat TEMA Dept Mech Engn P-3810193 Aveiro Portugal Univ Aveiro Ctr Res & Dev Math & Applicat CIDMA Dept Math P-3810193 Aveiro Portugal Intelligent Syst Associate Lab LASI Guimaraes Portugal Univ Aveiro Dept Mech Engn P-3810193 Aveiro Portugal

Control algorithms have been proposed based on knowledge related to nature -inspired mechanisms, including those based on the behavior of living beings. This paper presents a review focused on major breakthroughs carried out in the scope of applied control inspired by the gravitational attraction between bodies. A control approach focused on Artificial Potential Fields was identified, as well as four optimization metaheuristics: gravitational search algorithm, BlackHole algorithm, Multi -Verse Optimizer, and Galactic Swarm Optimization. A thorough analysis of ninety-one relevant papers was carried out to highlight their performance and to identify the gravitational and attraction foundations, as well as the universe laws supporting them. Included are their standard formulations, as well as their improved, modified, hybrid, cascade, fuzzy, chaotic and adaptive versions. Moreover, this review also deeply delves into the impact of universeinspired algorithms on control problems of dynamic systems, providing an extensive list of control -related applications, and their inherent advantages and limitations. Strong evidence suggests that gravitation -inspired and black -hole dynamic -driven algorithms can outperform other well-known algorithms in control engineering, even though they have not been designed according to realistic astrophysical phenomena and formulated according to astrophysics laws. Even so, they support future research directions towards the development of high -sophisticated control laws inspired by Newtonian/Einsteinian physics, such that effective control -astrophysics bridges can be established and applied in a wide range of applications.

关键词： Nature-inspired control gravitational search algorithm Black hole Artificial potential field Multi-verse optimizer Galactic swarm optimization Optimization Nonlinear control

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Hydrocarbon production dynamics forecasting using machine learning: A state-of-the-art review

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FUEL 2023年第1期337卷

作者： Liang, Bin Liu, Jiang You, Junyu Jia, Jin Pan, Yi Jeong, Hoonyoung Southwest Petr Univ State Key Lab Oil & Gas Reservoir Geol & Exploitat Chengdu 610500 Peoples R China Chongqing Univ Sci & Technol Chongqing 401331 Peoples R China Res Inst Petr Explorat & Dev Beijing 100086 Peoples R China Seoul Natl Univ Res Inst Energy & Resources Inst Engn Res Dept Energy Syst Engn Seoul 08826 South Korea

Accurate prediction of hydrocarbon production is crucial for the oil and gas industry. However, the strong heterogeneity of underground formation, the inconsistency in oil-gas-water distribution, and the complex flow mechanisms make hydrocarbon production forecasting (HPF) difficult, which leads to a high level of uncertainty in the prediction results. The explosion of machine learning (ML) methodologies that are capable of analyzing big data shed new light on HPF using production data. In this article, an in-depth review is provided regarding HPF using ML methodologies. Firstly, the merits and drawbacks of traditional HPF methods are analyzed and summarized. Then, the applications of ML algorithms in HPF are reviewed in detail, especially concentrating on artificial neural network, support vector machine, and ensemble learning. For each algorithm, the basic theory and its variants are first introduced, and its applications in HPF are comprehensively demonstrated subsequently. Finally, this article presents the challenge and prospects of machine-learning-based HPF. Sophisticated ML proxy models can be con-structed and employed to deal with an extended type of input data such that improving the efficacy of data utilization. On the other hand, deep learning models designed to handle time-series data can gain more attention. Modeling approaches for multivariate time-series hydrocarbon production data using deep neural networks with similar functionality to LSTM may lead to more accurate and computationally efficient production forecasting.

关键词： AI Artificial intelligence ANN Artificial neural network SVM Support vector machine DCA Decline curve analysis MFNN Multilayer feed-forward neural networks MLP Multilayer perceptron BP Back Propagation algorithm DNN Deep neural network LSTM Long short-term memory GAN Generative adversarial network CNN Convolutional neural network FCNN Fully connected neural network RNN Recurrent neural network BPANN Back propagation artificial neural network NN Neural network cDC-GAN Conditional deep convolutional generative neural network CONV-1D 1D convolutional neural network GA Genetic algorithm DE Differential evolution SA Simulated annealing GSA gravitational search algorithm PSO Particle swarm optimization MPA Marine predator algorithm ICA Imperialist competitive algorithm LM Levenberg Marquardt BRA Bayesian regularization algorithm MNN Multiple-neural-network HONN Higher-order neural network HOSO Higher-order synaptic operations MLMVN Multilayer neural network with multi-valued neurons DLSTM Deep long-short term memory EEMD Ensemble empirical mode decomposition FNN Feed-forward neural network BFGS Quasi-newton algorithm ORF Oil recovery factor GOR Gas oil ratio ADAM Adaptive moment estimation method WOPR well oil production rate SGD stochastic gradient descent SVR Support vector regression LSSVM Least squares support vector machine RSM Response surface method LR Linear regression RF Random forest AdaBoost Adaptive boosting GBDT Gradient boosting decision tree GBM Gradient-boosting machine MORF Multi-objective random forest GBT Gradient boosting tree PCA Principal component analysis RREE Reverse recursive feature elimination

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Tri-level regression testing using nature-inspired algorithms

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INNOVATIONS IN SYSTEMS AND SOFTWARE ENGINEERING 2021年第1期17卷 1-16页

作者： Bajaj, Anu Sangwan, Om Prakash Guru Jambheshwar Univ Sci & Technol Dept Comp Sci & Engn Hisar Haryana India

A software needs to be updated to survive in the customers' ever-changing demands and the competitive market. The modifications may produce undesirable changes that require retesting, known as regression testing, before releasing it in the public domain. This retesting cost increases with the growth of the software test suite. Thus, regression testing is divided into three techniques: test case prioritization, selection, and minimization to reduce costs and efforts. The efficiency and effectiveness of these techniques are further enhanced with the help of optimization techniques. Therefore, we present the regression testing using well-known algorithms, genetic algorithm, particle swarm optimization, a relatively new nature-inspired approach, gravitational search algorithm, and its hybrid with particle swarm optimization algorithm. Furthermore, we propose a tri-level regression testing, i.e., it performs all the three methods in succession. Nature-inspired algorithms prioritize the test cases on code coverage criteria. It is followed by selecting the modification-revealing test cases based on the proposed adaptive test case selection approach. The last step consists of the removal of redundant test cases. The hybrid algorithm performed well for the average percentage of statement coverage, and the efficiency of genetic algorithm and particle swarm optimization is better comparatively. The proposed test case selection method can select at least 75% modification-revealing test cases using nature-inspired algorithms. Additionally, it minimizes the test suite with full statement coverage and almost negligible fault coverage loss. Overall, the simulation results show that the proposed hybrid technique outperformed the other algorithms.

关键词： Regression testing Test case prioritization Test case selection Test case minimization search-based software testing Nature-inspired algorithms Genetic algorithm Particle swarm optimization gravitational search algorithm

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Non-Dominated Sorting-Based Hybrid Optimization Technique for Multi-Objective Hydrothermal Scheduling

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ENERGIES 2023年第5期16卷 2316页

作者： Nadakuditi, Gouthamkumar Pulluri, Harish Dahiya, Preeti Murthy, K. S. R. Varma, P. Srinivasa Bajaj, Mohit Altameem, Torki El-Shafai, Walid Fouda, Mostafa M. M. Govt India Minist Home Affairs Dept Fire Engn Natl Fire Serv Coll Nagpur 440013 India Anurag Univ Dept Elect & Elect Engn Hyderabad 500088 India Shri Vishwakarma Skill Univ Skill Fac Engn & Technol Elect Engn Gurugram 121102 India NIT Tadepallegudem 534101 Andhra Pradesh India Koneru Lakshmaiah Educ Fdn Dept Elect & Elect Engn Guntur 522302 India Graph Era Deemed Univ Dept Elect Engn Dehra Dun 248002 India Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan King Saud Univ Community Coll Comp Sci Dept Riyadh 11451 Saudi Arabia Menoufia Univ Fac Elect Engn Dept Elect & Elect Commun Engn Menoufia 32952 Egypt Idaho State Univ Coll Sci & Engn Dept Elect & Comp Engn Pocatello ID 83209 USA

Short-term hydrothermal scheduling problem plays an important role in maintaining a high degree of economy and reliability in power system operational planning. Since electric power generation from fossil fired plants forms a major part of hydrothermal generation mix, therefore their emission contributions cannot be neglected. Hence, multi-objective short term hydrothermal scheduling is formulated as a bi-objective optimization problem by considering (a) minimizing economical power generation cost, (b) minimizing environmental emission pollution, and (c) simultaneously minimizing both the conflicting objective functions. This paper presents a non-dominated sorting disruption-based oppositional gravitational search algorithm (NSDOGSA) to solve multi-objective short-term hydrothermal scheduling (MSHTS) problems and reveals that (i) the short-term hydrothermal scheduling problem is extended to a multi-objective short-term hydrothermal scheduling problem by considering economical production cost (EPC) and environmental pollution (EEP) simultaneously while satisfying various diverse constraints;(ii) by introducing the concept of non-dominated sorting (NS) in gravitational search algorithm (GSA), it can optimize two considered objectives such as EPC and EEP simultaneously and can also obtain a group of conflicting solutions in one trial simulation;(iii) in NSDOGSA, the objective function in terms fitness for mass calculation has been represented by its rank instead of its EPC & EEP values by using the NS approach;(iv) an elite external archive set is defined to keep the NS solutions with the idea of spread indicator;(v) the optimal schedule value is extracted by using fuzzy decision approach;(vi) a consistent handling strategy has been adopted to handle effectively the system constraints;(vii) finally, the NSDOGSA approach is verified on two test systems with valve point loading effects and transmission loss, and (viii) computational discussion show that the NSDOGSA gives

关键词： non-dominated sorting gravitational search algorithm economical environmental hydrothermal scheduling fuzzy decision-making opposition-based learning disruption operator

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Development of hybrid performance-based optimization algorithm for structures equipped with vibration damper devices

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ARCHIVES OF CIVIL AND MECHANICAL ENGINEERING 2023年第2期23卷 123-123页

作者： Ayyash, Al-jbury Najad Amer Hejazi, Farzad Univ Putra Malaysia Dept Civil Engn Seri Kembangan Malaysia Univ West England Fac Environm & Technol Bristol England

Nowadays, various types of vibration damping systems are being implemented in different buildings to diminish seismic effects on structures. However, engineers are faced with the challenging task of developing an optimum design for structures utilizing a proper type of damping device based on new techniques such as the performance-based design method. Therefore, this research was aimed at developing a multi-objective optimization algorithm by hybridizing the particle swarm optimization (PSO) and gravitational search algorithm (GSA) to obtain an optimum design for structures equipped with vibration damper devices based on the performance-based design method. Then, the developed hybrid algorithm (PSOGSA) would be capable of optimizing the damping system simultaneously with the optimized details of the structural sections, including the steel rebars, by satisfying all the design criteria. For this purpose, a special process for the design of structures equipped with vibration damper devices according to the performance-based design method was developed by considering of a wide range of vibration damping systems. The proposed PSOGSA optimization framework was then implemented to design a 12-storey reinforced concrete structure equipped with different types of dampers to minimize the structural weight while satisfying all the prescribed performance-based design acceptance criteria. The results indicated that the proposed optimization method was able to successfully optimize the details of the structural members as well as the type and properties of the damper, which significantly improved the structural response in terms of the formation of plastic hinges and the structural movements.

关键词： Hybrid optimization algorithm Particle swarm optimization gravitational search algorithm Seismic damper devices Performance-based design

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A Fusion algorithm of gravitational search and Tabu search

A Fusion Algorithm of Gravitational Search and Tabu Search

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作者： Ao Lou Rocket Force University of Engineering

In order to overcome the shortcomings of gravitational search algorithm(GSA),such as easily falling into local optimum traps and needing to be improved in accuracy,a fusion algorithm of gravitational search and tabu search(TS-GSA) is ***,the optimal solution of each iteration operation of GSA is regarded as a tabu search ***,a neighborhood search method is designed to search in depth between two historical extremes to avoid missing global optimum ***,tabu list is used to reduce the frequency of circuitous search and improve search *** simulation results show that TS-GSA has stronger global optimization ability than GSA,new hybrid populational-based algorithm with combination of particle swarm optimization and GSA(PSOGSA),adaptive gbest-guided GSA(GGSA),and can effectively overcome local optimum *** also show that TS-GSA can find the theoretical optimum solutions of some high-dimensional multi-modal functions,and has stronger robustness for low-dimensional multi-modal functions.

关键词： gravitational search algorithm tabu search algorithm algorithm fusion local optimum traps

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Research On MPPT Of PV System Based On CSAGAM

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淡江理工学刊 2023年第5期26卷 697-718页

作者： Tao Hou Shan Wang Mingxi Chen Yanzhang Zhao

The existing Maximum Power Point Tracking (MPPT) method has low tracking efficiency and poor stability. It is easy to fall into the Local Maximum Power Point (LMPP) in Partial Shading Condition (PSC), resulting in the degradation of output power quality and efficiency. It was found that various bio-inspired MPPT based optimization algorithms employ different mechanisms, and their performance in tracking the Global Maximum Power Point (GMPP) varies. Thus, a Cuckoo search algorithm based on the gravitational Acceleration Mechanism (CSAGAM) is put forward for the MPPT method of photovoltaic power generation. The method can improve the tracking speed by more than 65% compared with the traditional Cuckoo search algorithm (CSA), and the results of the study using this algorithm are compared with the popular Particle Swarm Optimization (PSO) and the gravitational search algorithm (GSA). CSAGAM has an average tracking efficiency of 99.99% and an average tracking time of 0.15s when tracking the GMPP, which improves PV power generation's efficiency and power quality.

关键词： partial shading condition PV system cuckoo search algorithm gravitational search algorithm global maximum power point

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