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Assessing the Stability of Slopes Using Vector-Sum-Based Numerical Manifold Method and pattern search algorithm

ROCK MECHANICS AND ROCK ENGINEERING 2022年第6期55卷 3659-3673页

作者： Yang, Yongtao Wu, Wenan Chinese Acad Sci Inst Rock & Soil Mech State Key Lab Geomech & Geotech Engn Wuhan 430071 Peoples R China Beijing Univ Technol Key Lab Urban Secur & Disaster Engn Minist Educ Beijing 100124 Peoples R China

The Numerical Manifold Method (NMM) has been considered as an effective analysis method for geotechnical problems. A vector-sum-based numerical manifold method (VSNMM) combined with a pattern search algorithm (PSA), termed VSNMM-PSA, is further proposed to assess the stability of slopes. In this numerical method, PSA is applied for the determination of failure surface location. With the stress field of the slope obtained from a NMM-based elastoplastic calculation, the vector sum method (VSM) is used to predict trial safety factors for a series of trial failure surfaces (TFSs) during the failure-surface determination process. Stability analyses about three standard slopes are investigated with the proposed VSNMM-PSA model. Numerical results indicate that slopes' safety factors and failure surfaces can be accurately determined through the proposed VSNMM-PSA method. The proposed VSNMM-PSA method provides an effective tool to evaluate slopes' stability, and may improve their designs.

关键词： Vector sum method Numerical manifold method Failure surface pattern search algorithm Safety factor

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pattern search algorithm for STATCOM based Damping Controller Design with Modified Local Input signal

Pattern Search Algorithm for STATCOM based Damping Controlle...

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Renewable Energy Integration into Smart Grids: A Multidisciplinary Approach to Technology Modelling and Simulation (ICREISG), International Conference on

作者： S. K. Barik S.K. Mohapatra A.K. Patra SEC Mayurbhanja Odisha India GCE Keonjhar Odisha India

ISBN: (数字)9781728170343

ISBN: (纸本)9781728170350

pattern search (PS) algorithm is proposed to design a STATCOM controller FACTS based transient stability in this paper. To avoid the disadvantage remote signal that may possibly affect consistency of the controller, a locally measured line active power equivalent to remote speed variation signal is the modified signal given to controller input. The stability performance is enhance by optimization of the controller's parameter of the power system. The responses of the projected controllers are applied to both SMIB and 3-machine power system considering several transient disturbances. On comparing with different input signal with different fault and three operating condition, the MATLAB simulation results are presented. It is concluded that the damping of the power system is greatly enhanced in the case of modified local signal is measured rather than local and remote input signals.

关键词： STATCOM pattern search algorithm damping oscillations power system stability Modified Local Signal Static compensator input signal Power system stability power systems damped oscillation Controllers pattern search absorption of shock single machine infinite bus system signals

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Design of reduced search space strategy based on integration of Nelder-Mead method and pattern search algorithm with application to economic load dispatch problem

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NEURAL COMPUTING & APPLICATIONS 2018年第12期30卷 3693-3705页

作者： Chouhdry, Zafar-ur-Rehman Hasan, Khalid M. Raja, Muhammad Asif Zahoor Univ Engn & Technol Dept Elect Engn Lahore Pakistan COMSATS Inst Informat Technol Dept Elect Engn Attock Campus Attock Pakistan

This paper presents a novel hybrid technique for the solution of economic load dispatch problem with valve point loading effect using Nelder-Mead (NM) simplex method and pattern search (PS) algorithm. Strength of globalized NM optimization algorithm has been employed to explore the search space for near optimal solution, and PS algorithm is used in combination with a search space reduction strategy, incorporating the principles of selection and stochastic reproduction, to fine-tune the result. The proposed technique has been applied to three different systems having 3, 13 and 40 generating units to demonstrate the application for small to large load dispatch set-up. The efficacy of the design scheme is established from comparison of the results with the state-of-the-art solvers, and it is found that the proposed scheme gives the best result in terms of mean cost while the average computational time is less than most of the reported methods.

关键词： Economic load dispatch Nelder-Mead method pattern search algorithm Hybrid techniques

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Hypersonic flight vehicle trajectory optimization using pattern search algorithm

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OPTIMIZATION AND ENGINEERING 2018年第1期19卷 125-161页

作者： Kumar, G. Naresh Ikram, Mohammad Sarkar, A. K. Talole, S. E. Def Res & Dev Lab Kanchanbagh PO Hyderabad 500058 Andhra Pradesh India Def Inst Adv Technol Dept Aerosp Engn Pune 411025 Maharashtra India

In this work, trajectory optimization of an aerodynamically controlled hypersonic boost glide class of flight vehicle is presented. In order to meet the mission constraints such as controllability, skin temperature, and terminal conditions etc., the trajectory is optimized using a pattern search algorithm with the lift to drag (L/D) ratio as a control parameter. It is brought out that the approach offers a viable tool for optimizing trajectories for the considered class of vehicles. Further, the effects of the constraints on trajectory shape and performance are studied and the analysis is used to bring out an optimal vehicle configuration at the initial stage of the design process itself. The research also reveals that the pattern search algorithm offers superior performance in comparison with the genetic algorithm for this class of optimization problem.

关键词： Hypersonic boost-glide vehicle Trajectory optimization pattern search algorithm Aerodynamic configuration design Skin temperature

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Hybrid adaptive "gbest'-guided gravitational search and pattern search algorithm for automatic generation control of multi-area power system

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IET GENERATION TRANSMISSION & DISTRIBUTION 2017年第13期11卷 3257-3267页

作者： Khadanga, Rajendra Kumar Kumar, Amit Natl Inst Sci & Technol Dept Elect Engn Berhampur Odisha India Natl Inst Technol Dept Elect Engn Rourkela Odisha India

In this research article, a maiden approach of hybrid adaptive gbest' guided gravitational search and pattern search (hGGSA-PS) optimization method are proposed for load frequency control (LFC) of multi-area interconnected power system considering the nonlinear effect of generation rate constraint (GRC). At first, the two area single stage thermal-thermal power system with conventional proportional integral derivative (PID) controller is analyzed and the parameters of the PID controller are optimized by the proposed technique. Initially, the gbest' guided gravitational search algorithm (GGSA) using integral time absolute error (ITAE) fitness function is used and then pattern search (PS) technique is employed to fine-tune the obtained best solution from the GGSA. The supremacy of the hGGSA-PS optimized PID controller is presented by comparing its results with other modern soft computing techniques. Later in order to demonstrate the robustness of the proposed controller, the sensitive analysis is performed. Finally, the proposed technique is extended to a two area multi-source power system. The parameters of the controller for each area are optimized using the novel hGGSA-PS technique. From the simulation results, it can be seen that the proposed technique has superior performance than the prior results with lesser settling time and different performance index values.

关键词： power generation control search problems optimisation thermal power stations load regulation frequency control nonlinear control systems three-term control hydrothermal power systems performance index power system interconnection Hybrid adaptive gbest-guided gravitational search pattern search algorithm hGGSA-PS optimisation method automatic generation control multi-area interconnected power system load frequency control nonlinear effect generation rate constraint two-area single-stage thermal-thermal power system proportional integral derivative controller PID controller integral time absolute error ITAE fitness function multisource power system hydrothermal power system tie-line power deviation performance index

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Efficient latency-and-energy-aware IoT-fog-cloud task orchestration: novel algorithmic approach with enhanced arithmetic optimization and pattern search

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International Journal of Information Technology (Singapore) 2024年第5期16卷 3311-3324页

作者： Srichandan, Suresh Kumar Majhi, Santosh Kumar Jena, Sudarson Mishra, Kaushik Rao, D. Chandrasekhar Sambalpur University Institute of Information Technology Sambalpur University Jyotivihar Odisha Burla 768019 India Veer Surendra Sai University of Technology Odisha Burla 768018 India Department of Computer Science and Information Technology Guru Ghasidas Vishwavidyalaya Koni C.G. Bilaspur 495009 India Gandhi Institute of Technology and Management Andhra Pradesh Visakhapatnam 530045 India

Nowadays, the load on datacenters has become more and more due to the unprecedented growth of diversified data from many IoT devices;hence, resource utilization has become more difficult. So, Cloud computing emerged as an appealing solution to leverage IoT devices' computational, memory, capacity and resource constraints. However, the physical gap between IoT devices and the Cloud datacentre resulted in latency overhead and incurred high energy consumption due to the centralized nature of datacenter. Thus, Fog computing is introduced as a complementary to Cloud computing to minimize the physical gap and thus, latency by bringing the computations closer to the end devices. However, the placement of tasks to a particular Fog node (FN)/cloud virtual machine (VM) is an NP-hard problem with much higher computational time. Thus, metaheuristic algorithms are a way out as they have a polynomial time complexity. The arithmetic optimization algorithm (AOA) along with the pattern search algorithm is one of the optimization algorithms, implemented in order to minimise the makespan and maximise resource utilization. This paper proposes an improved AOA when combined with a pattern search algorithm helps in migrating the tasks from an overloaded FN to an underloaded FN more effectively. Finally, the proposed algorithm is compared with similar optimization algorithms on various parameters. From the simulations, it is evident that the proposed AOA-PS-based task scheduling approach outperformed others with a percentage of improvement of 24.2%, 18.6%, 16%, 5.4%, and 23.4% for makespan, resource utilization, response time, energy consumption, and degree of imbalance, respectively. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Arithmetic optimization algorithm Cloud computing Fog computing IoT pattern search algorithm Resource management Task orchestration

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Optimization of Source Modeling From Phaseless Near-Field Scanning Based on SVD-Defined Eigenmodes

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IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY 2024年第6期66卷 1876-1887页

作者： Tsai, Min-Hsu Qian, Hong-Wen Chen, Wei-Kai Huang, Ming-Chung Liu, Jen-Chieh Wu, Ruey-Beei Natl Taiwan Univ Dept Elect Engn Taipei 10617 Taiwan Natl Taiwan Univ Grad Inst Commun Engn Taipei 10617 Taiwan Realtek Semicond Corp Hsinchu 30076 Taiwan Himax Technol Xinshi 74148 Taiwan

This article proposes a procedure for reconstructing dipole moment models from magnitude-only near-field scanned magnetic fields. Preprocessing uses a finite-impulse response filter to deblur the 2-D divergence of the magnetic fields to locate the dipoles. The procedure defines the modes of the dipole distribution using singular value decomposition. Based on mode decomposition, the modal coefficients are optimized through a pattern search algorithm to obtain the dipole moments. Compared with existing methods, this method reconstructs better phase information in high-noise situations. Using mode decomposition circumvents the problem of a limited number of dipoles that can be solved by existing optimization methods. Real experiments using high-resolution near-field scanned data show that the main emission sources of individual traces inside an integrated circuit (IC) chip can be distinguished through the proposed procedure.

关键词： Magnetic moments Magnetic resonance imaging Optimization Finite impulse response filters Noise Integrated circuit modeling Phase measurement Electromagnetic interference (EMI) equivalent dipole model near-field scanning optimization pattern search algorithm phase-retrieval singular value decomposition (SVD)

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Dynamic Sensor Scheduling for Data Size Reduction in a Sensor Cloud System Based on Minimum Reconstruction Error

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WIRELESS PERSONAL COMMUNICATIONS 2024年第3期135卷 1423-1447页

作者： Shylashree, N. Kumar, Sachin VTU RV Coll Engn Dept Elect & Commun Bengaluru 560059 India South Ural State Univ Big Data & Machine Learning Lab Chelyabinsk Russia

Sensing and subsequent analysis of the environmental data of a given geographical area is an essential requisite for the planned development of that region. Nowadays, IoT Sensor-Cloud ecosystem has been adopted to collect data from IoT sensors and transmit it to the chosen Cloud Server for further processing and dissemination. In a large Wireless Sensor Network formed by the IoT sensors, there will be a significant amount of redundancy in the dataset when the nodes are placed closely, and the sensed data varies slowly and gradually with regard to time and space. Then, avoiding redundant data transmission can lead to lower energy consumption and communication overhead. Adaptive subset selection of sensor nodes for data size reduction in a Wireless Sensor Network is an approach to efficiently managing the amount of data transmitted within the network. Then, in the current time schedule, it is possible to optimally select a subset of the sensor nodes for data collection without very much affecting the overall data fidelity. An optimal sensor node subset selection scheme that reduces the communication load with minimum information loss is proposed to achieve this task. The unselected nodes are put in sleep mode, which consequently results in lower sensor energy expenditure. The subset selection algorithm is implemented based on the derivative-free pattern search optimizer that minimizes the reconstruction error during the associated extrapolation. This approach differs entirely from the Compressive Data Gathering approach. The simulation results reveal that the performance of the proposed scheme is superior to other similar competitive methods in terms of the mean square error, which is found to be 1.95, with the percentage participation nodes equal to 50% and when the sensor data is uniformly distributed over 20 and 30 units.

关键词： pattern search algorithm Grid data extrapolation Minimum reconstruction error Keyframes Intermediate frames Wakeup duty cycle

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Genetic algorithm-Driven Cover Set Scheduling for Longevity in Wireless Sensor Networks

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第3期16卷 1104-1112页

作者： Larhlimi, Ibtissam Lmkaiti, Mansour Lachgar, Maryem Ouchitachen, Hicham Darif, Anouar Limati, Hicham Mouncif Sultan Moulay Slimane Univ LAB Polydisciplinary Fac Khouribga Morocco

This paper aims to develop an efficient scheduling approach based on Genetic algorithms to optimize energy consumption and maximize the operational lifetime of Wireless cial for prolonging the operational lifespan of wireless sensor networks (WSNs) that include a substantial number of sensors. Simultaneously activating all sensors results in a fast depletion of energy, thus diminishing the overall lifespan of the network. To address this issue, it is necessary to schedule sensor activity in an effective manner. This task, known as the maximum coverage set scheduling (MCSS) problem, is highly complex and has been demonstrated to be NP-hard. This article presents a customized genetic algorithm designed to tackle the MCSS problem, aiming to improve the longevity of Wireless Sensor Networks (WSNs). Our methodology effectively detects and enhances combinations of coverage sets and their corresponding schedules. The program incorporates key criteria such as the detection ranges of individual sensors, their energy levels, and activity durations to optimize the overall energy efficiency and operational sustainability of the network. The performance of the suggested algorithm is assessed through simulations and compared to that of the Greedy algorithm and the pattern search algorithm. The results indicate that our genetic algorithm not only maximizes network lifetime but also enhances the efficiency and efficacy of solving the MCSS problem. This represents a significant improvement in managing the energy consumption in WSNs.

关键词： Maximum network lifetime wireless sensor network coverage sets scheduling genetic algorithm pattern search algorithm

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Smart carrot chasing guidance law for path following of unmanned surface vehicles

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART M-JOURNAL OF ENGINEERING FOR THE MARITIME ENVIRONMENT 2024年第3期238卷 567-576页

作者： Unal, Osman Akkas, Nuri Atali, Gokhan Ozkan, Sinan Serdar Sakarya Univ Appl Sci Dept Mech Engn Sakarya Turkiye Sakarya Univ Appl Sci Dept Mechatron Engn Sakarya Turkiye Sakarya Univ Appl Sci Dept Mech Engn TR-54050 Serdivan Sakarya Turkiye

Carrot chasing guidance law is one of the most widely used path following algorithms due to its simplicity and ease of implementation;however, it has a fixed parameter which leads to large cross-tracking errors during different navigational conditions. This study proposes an innovative approach to carrot chasing algorithm to minimize cross-tracking errors. pattern search optimization technique is integrated with carrot chasing guidance law to determine unique virtual target points obtained by flexible parameters instead of a fixed parameter. Proposed smart carrot chasing guidance law (SCCGL) provides stable and accurate path following even for different navigational conditions of unmanned surface vehicle (USV). To the best of our knowledge, we are the first to apply pattern search optimization technique to carrot chasing guidance law while USV is performing multi-tasks of predefined paths. This novelty significantly reduces both cross tracking errors and computational costs. Firstly, SCCGL is tested and compared with traditional carrot chasing algorithm in the numerical simulator for several navigational conditions such as different lists of waypoints, different initial locations, and different maximum turning rates of USV. SCCGL automatically determines optimal parameters to make stable and accurate navigation. SCCGL significantly reduces cross tracking errors compared to classical carrot chasing algorithm. This is the first contribution of this paper. Secondly, genetic algorithm optimization method has been implemented to carrot chasing guidance law instead of pattern search optimization technique. Genetic algorithm causes the total simulation time to be quite long. The proposed SCCGL (pattern search integrated carrot chasing guidance law) gives optimum results 20 times faster than the genetic algorithm. This is the second and main contribution of developed SCCGL method. It is observed that SCCGL provides best navigation with minimum cross-tracking errors and mini

关键词： Path following algorithm carrot-chasing guidance law pattern search algorithm cross-tracking error unmanned surface vehicle

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