Journal articles: 'Swarm verification'

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Holzmann, Gerard J., Rajeev Joshi, and Alex Groce. "Swarm Verification Techniques." IEEE Transactions on Software Engineering 37, no. 6 (November 2011): 845–57. http://dx.doi.org/10.1109/tse.2010.110.

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LU, Nan, Xiaodong WANG, Zheng TANG, and Pei HE. "Modeling method of unmanned aerial vehicle swarm behavior based on spatiotemporal hybrid Petri net." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 40, no. 4 (August 2022): 812–18. http://dx.doi.org/10.1051/jnwpu/20224040812.

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The more and more widely used UAV swarm operations have received great attention in the new global military revolution of informatization, and the integrated modeling of UAV swarms has great significance and value for the testing and verification of combat modes. Aiming at the modeling and simulation requirements of combat scenarios, taking the collaborative combat process of heterogeneous UAV swarms as the research object, starting from the modeling of a single UAV, on the basis of the formalization and mathematical description of the single combat process, this paper employs Petri nets based on the hybridization of time and space to describe the discrete states and continuous processes of heterogeneous UAV swarm systems, and effectively solves the problems of the fusion between physics and computing processes, and modeling of interactive events in swarm systems. UPPAAL is selected to formally verify the modeling of UAV swarm strike mission, which shows that the proposed modeling method is feasible and effective.

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Wijs, Anton. "Informed Swarm Verification of Infinite-State Systems." Electronic Proceedings in Theoretical Computer Science 73 (November 11, 2011): 19. http://dx.doi.org/10.4204/eptcs.73.4.

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Dixon, Clare, Alan F. T. Winfield, Michael Fisher, and Chengxiu Zeng. "Towards temporal verification of swarm robotic systems." Robotics and Autonomous Systems 60, no. 11 (November 2012): 1429–41. http://dx.doi.org/10.1016/j.robot.2012.03.003.

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Sharmila D , A. V. Pra.bu, N. Selvaganesh,. "AUTHORSHIP VERIFICATION USING MODIFIED PARTICLE SWARM OPTIMIZATION ALGORITHM." Psychology and Education Journal 58, no. 1 (January 15, 2021): 4262–66. http://dx.doi.org/10.17762/pae.v58i1.1492.

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Digital forensics is the study of recovery and investigation of the materials found in digital devices, mainly in computers. Forensic authorship analysis is a branch of digital forensics. It includes tasks such as authorship attribution, authorship verification, and author profiling. In Authorship verification, with a given a set of sample documents D written by an author A and an unknown document d, the task is to find whether document d is written by A or not. Authorship verification has been previously done using genetic algorithms, SVM classifiers, etc. The existing system creates an ensemble model by combining the features based on the similarity scores, and the parameter optimization was done using a grid search. The accuracy of verification using the grid search method is 62.14%. The time complexity is high as the system tries all possible combinations of the features during the ensemble model's construction. In the proposed work, Modified Particle Swarm Optimization (MPSO) is used to construct the classification model in the training phase, instead of the ensemble model. In addition to the combination of linguistic and character features, Average Sentence Length is used to improve the verification task accuracy. The accuracy of verification has been improved to 63.38%.

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Huang, Ai Ming, and Mao Ling Pen. "Multi Biometrics Fusion Identity Verification Based on Particle Swarm Optimization." Applied Mechanics and Materials 44-47 (December 2010): 3195–99. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3195.

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In recent years, biometrics has become one of the most promising identity verification technologies. For the limitations, it is difficult for single mode biometrics to meet requirements of modern identity verification. The paper introduced several common biometrics verification methods and procedures. The limitations of single mode biometrics were also provided and data fusion technology was introduced to solve the problem. On the basis of this, Particle Swarm Optimization (PSO) neural network algorithm was used to construct multi biometrics verification system. The results of experiment based on the method shows that it can achieve better identity verification result and meet requirements of practical applications.

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Wang, Chuanyun, Yang Su, Jingjing Wang, Tian Wang, and Qian Gao. "UAVSwarm Dataset: An Unmanned Aerial Vehicle Swarm Dataset for Multiple Object Tracking." Remote Sensing 14, no. 11 (May 28, 2022): 2601. http://dx.doi.org/10.3390/rs14112601.

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In recent years, with the rapid development of unmanned aerial vehicles (UAV) technology and swarm intelligence technology, hundreds of small-scale and low-cost UAV constitute swarms carry out complex combat tasks in the form of ad hoc networks, which brings great threats and challenges to low-altitude airspace defense. Security requirements for low-altitude airspace defense, using visual detection technology to detect and track incoming UAV swarms, is the premise of anti-UAV strategy. Therefore, this study first collected many UAV swarm videos and manually annotated a dataset named UAVSwarm dataset for UAV swarm detection and tracking; thirteen different scenes and more than nineteen types of UAV were recorded, including 12,598 annotated images—the number of UAV in each sequence is 3 to 23. Then, two advanced depth detection models are used as strong benchmarks, namely Faster R-CNN and YOLOX. Finally, two state-of-the-art multi-object tracking (MOT) models, GNMOT and ByteTrack, are used to conduct comprehensive tests and performance verification on the dataset and evaluation metrics. The experimental results show that the dataset has good availability, consistency, and universality. The UAVSwarm dataset can be widely used in training and testing of various UAV detection tasks and UAV swarm MOT tasks.

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Huang, Yixin, Xiaojia Xiang, Han Zhou, Dengqing Tang, and Yihao Sun. "Online Identification-Verification-Prediction Method for Parallel System Control of UAVs." Aerospace 8, no. 4 (April 2, 2021): 99. http://dx.doi.org/10.3390/aerospace8040099.

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In order to solve the problem of how to efficiently control a large-scale swarm Unmanned Aerial Vehicle (UAV) system, which performs complex tasks with limited manpower in a non-ideal environment, this paper proposes a parallel UAV swarm control method. The key technology of parallel control is to establish a one-to-one artificial UAV system corresponding to the aerial swarm UAV on the ground. This paper focuses on the computational experiments algorithm for artificial UAV system establishment, including data processing, model identification, model verification and state prediction. Furthermore, this paper performs a comprehensive flight mission with four common modes (climbing, level flighting, turning and descending) for verification. The results of the identification experiment present a good consistency between the outputs of the refined dynamics model and the real flight data. The prediction experiment results show that the prediction method in this paper can basically guarantee that the prediction states error is kept within 10% about 16 s.

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V. Gayetri Devi, S., C. Nalini, and N. Kumar. "An efficient software verification using multi-layered software verification tool." International Journal of Engineering & Technology 7, no. 2.21 (April 20, 2018): 454. http://dx.doi.org/10.14419/ijet.v7i2.21.12465.

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Rapid advancements in Software Verification and Validation have been critical in the wide development of tools and techniques to identify potential Concurrent bugs and hence verify the software correctness. A concurrent program has multiple processes and shared objects. Each process is a sequential program and they use the shared objects for communication for completion of a task. The primary objective of this survey is retrospective review of different tools and methods used for the verification of real-time concurrent software. This paper describes the proposed tool ‘F-JAVA’ for multithreaded Java codebases in contrast with existing ‘FRAMA-C’ platform, which is dedicated to real-time concurrent C software analysis. The proposed system is comprised of three layers, namely Programming rules generation stage, Verification stage with Particle Swarm Optimization (PSO) algorithm, and Performance measurement stage. It aims to address some of the challenges in the verification process such as larger programs, long execution times, and false alarms or bugs, and platform independent code verification

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Zhang, Hong, and Masumi Ishikawa. "The performance verification of an evolutionary canonical particle swarm optimizer." Neural Networks 23, no. 4 (May 2010): 510–16. http://dx.doi.org/10.1016/j.neunet.2009.12.002.

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Yin, Lirong, Lei Wang, Wenfeng Zheng, Lijun Ge, Jiawei Tian, Yan Liu, Bo Yang, and Shan Liu. "Evaluation of Empirical Atmospheric Models Using Swarm-C Satellite Data." Atmosphere 13, no. 2 (February 9, 2022): 294. http://dx.doi.org/10.3390/atmos13020294.

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Swarm-C satellite, a new instrument for atmospheric study, has been the focus of many studies to evaluate its usage and accuracy. This paper takes the Swarm-C satellite as a research object to verify the Swarm-C accelerometer’s inversion results. This paper uses the two-row orbital elements density inversion to verify the atmospheric density accuracy results of the Swarm-C satellite accelerometer. After the accuracy of the satellite data is verified, this paper conducts comparative verification and empirical atmospheric model evaluation experiments based on the Swarm-C accelerometer’s inversion results. After comparing with the inversion results of the Swarm-C semi-major axis attenuation method, it is found that the atmospheric density obtained by inversion using the Swarm-C accelerometer is more dynamic and real-time. It shows that with more available data, the Swarm-C satellite could be a new high-quality instrument for related studies along with the well-established satellites. After evaluating the performance of the JB2008 and NRLMSISE-00 empirical atmospheric models using the Swarm-C accelerometer inversion results, it is found that the accuracy and real-time performance of the JB2008 model at the altitude where the Swarm-C satellite is located are better than the NRLMSISE-00 model.

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Babić, Anja, Ivan Lončar, Barbara Arbanas, Goran Vasiljević, Tamara Petrović, Stjepan Bogdan, and Nikola Mišković. "A Novel Paradigm for Underwater Monitoring Using Mobile Sensor Networks." Sensors 20, no. 16 (August 17, 2020): 4615. http://dx.doi.org/10.3390/s20164615.

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This paper presents a novel autonomous environmental monitoring methodology based on collaboration and collective decision-making among robotic agents in a heterogeneous swarm developed within the project subCULTron, tested in a realistic marine environment. The swarm serves as an underwater mobile sensor network for exploration and monitoring of large areas. Different robotic units enable outlier and fault detection, verification of measurements and recognition of environmental anomalies, and relocation of the swarm throughout the environment. The motion capabilities of the robots and the reconfigurability of the swarm are exploited to collect data and verify suspected anomalies, or detect potential sensor faults among the swarm agents. The proposed methodology was tested in an experimental setup in the field in two marine testbeds: the Lagoon of Venice, Italy, and Biograd an Moru, Croatia. Achieved experimental results described in this paper validate and show the potential of the proposed approach.

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Cosar, Mustafa, and Harun Emre Kiran. "Verification of Localization via Blockchain Technology on Unmanned Aerial Vehicle Swarm." Computing and Informatics 40, no. 2 (2021): 428–45. http://dx.doi.org/10.31577/cai_2021_2_428.

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Zhang, Guo you, and Jian chao Zeng. "Analysis and verification of terrain coverage algorithm based on wasp swarm." International Journal of Modelling, Identification and Control 14, no. 4 (2011): 250. http://dx.doi.org/10.1504/ijmic.2011.043147.

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Kida, Tomoha, Yuichiro Sueoka, Hiro Shigeyoshi, Yusuke Tsunoda, Yasuhiro Sugimoto, and Koichi Osuka. "Verification of Acoustic-Wave-Oriented Simple State Estimation and Application to Swarm Navigation." Journal of Robotics and Mechatronics 33, no. 1 (February 20, 2021): 119–28. http://dx.doi.org/10.20965/jrm.2021.p0119.

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Cooperative swarming behavior of multiple robots is advantageous for various disaster response activities, such as search and rescue. This study proposes an idea of communication of information between swarm robots, especially for estimating the orientation and direction of each robot, to realize decentralized group behavior. Unlike the conventional camera-based systems, we developed robots equipped with a speaker array system and a microphone system to utilize the time difference of arrival (TDoA). Sound waves outputted by each robot was used to estimate the relative direction and orientation. In addition, we attempt to utilize two characteristics of sound waves in our experiments, namely, diffraction and superposition. This paper also investigates the accuracy of state estimation in cases where the robots output sounds simultaneously and are not visible to each other. Finally, we applied our method to achieve behavioral control of a swarm of five robots, and demonstrated that the leader robot and follower robots exhibit good alignment behavior. Our methodology is useful in scenarios where steps or obstacles are present, in which cases camera-based systems are rendered unusable because they require each robot to be visible to each other in order to collect or share information. Furthermore, camera-based systems require expensive devices and necessitate high-speed image processing. Moreover, our method is applicable for behavioral control of swarm robots in water.

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Zhang, Yu Xin, and Yu Liu. "Application of BP Neural Network Based on Immune Particle Swarm Optimization for Fault Diagnosis of Power Transformer." Applied Mechanics and Materials 448-453 (October 2013): 3605–9. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.3605.

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Cloing and hypermutation of immune theory were used in optimization on particle swarm optimization (PSO), an immune particle swarm optimization (IPSO) algorithm was proposed , which overcome the problem of premature convergence on PSO. IPSO was used in BP Neural Network training to overcome slow convergence speed and easily getting into local dinky value of gradient descent algorithm. BP Neural Network trained by IPSO was used to fault diagnosis of power transformer, it has high accuracy after experimental verification and to meet the power transformer diagnosis engineering requirements.

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Chou, Fu-I., Tian-Hsiang Huang, Po-Yuan Yang, Chin-Hsuan Lin, Tzu-Chao Lin, Wen-Hsien Ho, and Jyh-Horng Chou. "Controllability of Fractional-Order Particle Swarm Optimizer and Its Application in the Classification of Heart Disease." Applied Sciences 11, no. 23 (December 5, 2021): 11517. http://dx.doi.org/10.3390/app112311517.

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This study proposes a method to improve fractional-order particle swarm optimizer to overcome the shortcomings of traditional swarm algorithms, such as low search accuracy in a high-dimensional space, falling into local minimums, and nonrobust results. In natural phenomena, our controllable fractional-order particle swarm optimizer can explore search spaces in detail to obtain high resolutions. Moreover, the proposed algorithm is memorable, i.e., position updates focus on the particle position of previous and last generations, rendering it conservative when updating the position, and obtained results are robust. For verifying the algorithm’s effectiveness, 11 test functions compare the average value, overall best value, and standard deviation of the controllable fractional-order particle swarm optimizer and controllable particle swarm optimizer; experimental results show that the stability of the former is better than the latter. Furthermore, the solution position found by the controllable fractional-order particle swarm optimizer is more reliable. Therefore, the improved method proposed herein is effective. Moreover, this research describes how a heart disease prediction application uses the optimizer we proposed to optimize XGBoost hyperparameters with custom target values. The final verification of the obtained prediction model is effective and reliable, which shows the controllability of our proposed fractional-order particle swarm optimizer.

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Wang, Xihui, Houtao Chen, Xiaoxing Zhu, Zhijie Wang, Xin Xun, and Honghao He. "Modeling of coordination control system for supercritical unit under wet state operating condition." Journal of Physics: Conference Series 2378, no. 1 (December 1, 2022): 012038. http://dx.doi.org/10.1088/1742-6596/2378/1/012038.

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Abstract The coordination control system model of supercritical unit under wet state operating condition is established in this paper. Based on the huge historical operating data of a unit, the static parameters and dynamic parameters of the model are solved by data statistics and regression and genetic algorithm and particle swarm optimization algorithm, respectively. Steady-state operating condition points verification, open-loop step response verification, and historical operation data closed-loop verification are conducted. The results show that the model is accurate and can be used for control system designing. For steady-state condition point verification, the relative error between the model output results and the actual data is less than 3.5%. The open-loop step disturbance test can objectively reflect the response characteristics of the unit. As to the closed-loop verification, the relative error between the model output results and the actual data is within 3%.

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Jia, Dongbao, Weixiang Xu, Dengzhi Liu, Zhongxun Xu, Zhaoman Zhong, and Xinxin Ban. "Verification of Classification Model and Dendritic Neuron Model Based on Machine Learning." Discrete Dynamics in Nature and Society 2022 (July 4, 2022): 1–14. http://dx.doi.org/10.1155/2022/3259222.

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Artificial neural networks have achieved a great success in simulating the information processing mechanism and process of neuron supervised learning, such as classification. However, traditional artificial neurons still have many problems such as slow and difficult training. This paper proposes a new dendrite neuron model (DNM), which combines metaheuristic algorithm and dendrite neuron model effectively. Eight learning algorithms including traditional backpropagation, classic evolutionary algorithms such as biogeography-based optimization, particle swarm optimization, genetic algorithm, population-based incremental learning, competitive swarm optimization, differential evolution, and state-of-the-art jSO algorithm are used for training of dendritic neuron model. The optimal combination of user-defined parameters of model has been systemically investigated, and four different datasets involving classification problem are investigated using proposed DNM. Compared with common machine learning methods such as decision tree, support vector machine, k-nearest neighbor, and artificial neural networks, dendritic neuron model trained by biogeography-based optimization has significant advantages. It has the characteristics of simple structure and low cost and can be used as a neuron model to solve practical problems with a high precision.

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Yang, Wenda, Minggong Wu, Xiangxi Wen, and Kexin Bi. "A Joint Optimization Method for Cooperative Detection Resources Based on Channel Capacity." International Journal of Aerospace Engineering 2022 (August 18, 2022): 1–16. http://dx.doi.org/10.1155/2022/8418426.

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Aiming at the resource optimization problem in the cooperative detection task, the objective function is constructed based on the channel capacity, and the artificial bee colony (ABC) algorithm is improved to realize the joint optimization of the UAV swarm trajectory and radiation power. Firstly, a multiple input multiple output (MIMO) cooperative detection model is constructed. Then, based on the perspective of information theory, the channel capacity of the cooperative detection model is derived and used as the objective function for optimizing the detection resources of UAV swarm. Then, the factors affecting the objective function are sorted out and analyzed one by one, and the constraints are clarified. Aiming at the shortcomings of ABC algorithm, its search strategy and parameter optimization method are improved. A joint optimization process of UAV swarm trajectory and radiated power based on improved ABC algorithm is constructed. Finally, through simulation verification and algorithm comparison, it shows that the algorithm in this paper can improve the perception ability of cooperative detection of UAV swarm.

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YAMAMOTO, Kentaro, Ryosuke MATSUZAKI, and Akira TODOROKI. "Experimental verification of delamination detection in CFRP laminates using Crack Swarm Inspection." Proceedings of the Materials and processing conference 2016.24 (2016): 322. http://dx.doi.org/10.1299/jsmemp.2016.24.322.

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Chen, Yung-Hsiang, and Yung-Yue Chen. "Trajectory Tracking Design for a Swarm of Autonomous Mobile Robots: A Nonlinear Adaptive Optimal Approach." Mathematics 10, no. 20 (October 20, 2022): 3901. http://dx.doi.org/10.3390/math10203901.

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This research presents a nonlinear adaptive optimal control approach to the trajectory tracking problem of a swarm of autonomous mobile robots. Mathematically, finding an analytical adaptive control solution that meets the H2 performance index for the trajectory tracking problem when controlling a swarm of autonomous mobile robots is an almost impossible task, due to the great complexity and high dimensions of the dynamics. For deriving an analytical adaptive control law for this tracking problem, a particular formulation for the trajectory tracking error dynamics between a swarm of autonomous mobile robots and the desired trajectory is made via a filter link. Based on this prior analysis of the trajectory tracking error dynamics, a closed-form adaptive control law is analytically derived from a high-dimensional nonlinear partial differential equation, which is equivalent to solving the trajectory tracking problem of a swarm of autonomous mobile robots with respect to an H2 performance index. This delivered adaptive nonlinear control solution offers the advantages of a simple control structure and good energy-saving performance. From the trajectory tracking verification, this proposed control approach possesses satisfactory trajectory tracking performance for a swarm of autonomous mobile robots, even under the effects of huge modeling uncertainties.

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Ferrer, Eduardo Castelló, Thomas Hardjono, Alex Pentland, and Marco Dorigo. "Secure and secret cooperation in robot swarms." Science Robotics 6, no. 56 (July 28, 2021): eabf1538. http://dx.doi.org/10.1126/scirobotics.abf1538.

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The importance of swarm robotics systems in both academic research and real-world applications is steadily increasing. However, to reach widespread adoption, new models that ensure the secure cooperation of large groups of robots need to be developed. This work introduces a method to encapsulate cooperative robotic missions in an authenticated data structure known as a Merkle tree. With this method, operators can provide the “blueprint” of the swarm’s mission without disclosing its raw data. In other words, data verification can be separated from data itself. We propose a system where robots in a swarm, to cooperate toward mission completion, have to “prove” their integrity to their peers by exchanging cryptographic proofs. We show the implications of this approach for two different swarm robotics missions: foraging and maze formation. In both missions, swarm robots were able to cooperate and carry out sequential tasks without having explicit knowledge about the mission’s high-level objectives. The results presented in this work demonstrate the feasibility of using Merkle trees as a cooperation mechanism for swarm robotics systems in both simulation and real-robot experiments, which has implications for future decentralized robotics applications where security plays a crucial role.

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Du, Yexin, Li Qing, and Jie He. "Research on Particle Swarm Fusion Sliding Mode Tracking Decoding Technology for Rotary Transformer." Journal of Physics: Conference Series 2428, no. 1 (February 1, 2023): 012029. http://dx.doi.org/10.1088/1742-6596/2428/1/012029.

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Abstract A sliding mode tracking fusion particle swarm optimization (PSO) was proposed to solve the soft decoding problem of the rotary transformer. Firstly, the principle of soft decoding of the rotary transformer was expounded and analyzed. Then the mathematical model of the sliding mode tracking and decoding system was established. Based on sliding mode tracking and decoding, the parameters in the model were selected iteratively by using a particle swarm optimization algorithm according to the fitness value function. Finally, the simulation environment was built by Simulink and S-function for simulation verification. The simulation results show that the decoding scheme based on particle swarm optimization and sliding mode tracking can track and decode the rotating transformers at different speeds, such as uniform speed, acceleration speed, and sines/cosines speed, without steady-state error and with certain anti-interference.

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Lin, Cheng-Jian, Shiou-Yun Jeng, Hsueh-Yi Lin, and Cheng-Yi Yu. "Design and Verification of an Interval Type-2 Fuzzy Neural Network Based on Improved Particle Swarm Optimization." Applied Sciences 10, no. 9 (April 27, 2020): 3041. http://dx.doi.org/10.3390/app10093041.

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In this study, we proposed an interval type-2 fuzzy neural network (IT2FNN) based on an improved particle swarm optimization (PSO) method for prediction and control applications. The noise-suppressing ability of the proposed IT2FNN was superior to that of the traditional type-1 fuzzy neural network. We proposed dynamic group cooperative particle swarm optimization (DGCPSO) with superior local search ability to overcome the local optimum problem of traditional PSO. The proposed model and related algorithms were verified through the accuracy of prediction and wall-following control of a mobile robot. Supervised learning was used for prediction, and reinforcement learning was used to achieve wall-following control. The experimental results demonstrated that DGCPSO exhibited superior prediction and wall-following control.

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Chen, Yung-Hsiang, and Shi-Jer Lou. "Control Design of a Swarm of Intelligent Robots: A Closed-Form H2 Nonlinear Control Approach." Applied Sciences 10, no. 3 (February 5, 2020): 1055. http://dx.doi.org/10.3390/app10031055.

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A closed-form H2 approach of a nonlinear trajectory tracking design and practical implementation of a swarm of wheeled mobile robots (WMRs) is presented in this paper. For the nonlinear trajectory tracking problem of a swarm of WMRs, the design purpose is to point out a closed-form H2 nonlinear control method that analytically fulfills the H2 control performance index. The key and primary contribution of this research is a closed-form solution with a simple control structure for the trajectory tracking design of a swarm of WMRs is an absolute achievement and practical implementation. Generally, it is challenging to solve and find out the closed-form solution for this nonlinear trajectory tracking problem of a swarm of WMRs. Fortunately, through a sequence of mathematical operations for the trajectory tracking error dynamics between the control of a swarm of WMRs and desired trajectories, this H2 trajectory tracking problem is equal to solve the nonlinear time-varying Riccati-like equation. Additionally, the closed-form solution of this nonlinear time-varying Riccati-like equation will be acquired with a straightforward form. Finally, for simulation-controlled performance of this H2 proposed method, two testing scenarios, circular and S type reference trajectories, were applied to performance verification.

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Hashemi, Seyyed Mohammad, and Iraj Rahmani. "Numerical Comparison of the Performance of Genetic Algorithm and Particle Swarm Optimization in Excavations." Civil Engineering Journal 4, no. 9 (September 30, 2018): 2186. http://dx.doi.org/10.28991/cej-03091149.

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Today, the back analysis methods are known as reliable and effective approaches for estimating the soil strength parameters in the site of project. The back analysis can be performed by genetic algorithm and particle swarm optimization in the form of an optimization process. In this paper, the back analysis is carried out using genetic algorithm and particle swarm optimization in order to determine the soil strength parameters in an excavation project in Tehran city. The process is automatically accomplished by linking between MATLAB and Abaqus software using Python programming language. To assess the results of numerical method, this method is initially compared with the results of numerical studies by Babu and Singh. After the verification of numerical results, the values of the three parameters of elastic modulus, cohesion and friction angle (parameters of the Mohr–Coulomb model) of the soil are determined and optimized for three soil layers of the project site using genetic algorithm and particle swarm optimization. The results optimized by genetic algorithm and particle swarm optimization show a decrease of 72.1% and 62.4% in displacement differences in the results of project monitoring and numerical analysis, respectively. This research shows the better performance of genetic algorithm than particle swarm optimization in minimization of error and faster success in achieving termination conditions.

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CHOI, Han-Yong, Wataru SHINYA, and Kazuho YOSHIMOTO. "812 Verification and application of Particle Swarm Optimization for the facility location problem." Proceedings of Conference of Chugoku-Shikoku Branch 2009.47 (2009): 279–80. http://dx.doi.org/10.1299/jsmecs.2009.47.279.

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Shieh, Horng-Lin, Cheng-Chien Kuo, and Chin-Ming Chiang. "Modified particle swarm optimization algorithm with simulated annealing behavior and its numerical verification." Applied Mathematics and Computation 218, no. 8 (December 2011): 4365–83. http://dx.doi.org/10.1016/j.amc.2011.10.012.

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Wang, Haixin, Shengsong Wei, Xin Chen, Mei Zhu, and Zuhe Wang. "Hybrid Differential Evolution Particle Swarm Optimization Algorithm for Solving Resource Leveling Problem of Multi-project with Fixed Duration." International Journal of Circuits, Systems and Signal Processing 16 (March 11, 2022): 801–10. http://dx.doi.org/10.46300/9106.2022.16.99.

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This paper attempts to substitute Resource Leveling Problem (RLP) into multi-project environment and construct Resource Leveling Problem of Multi-project (RLPMP) model with the goal of minimizing the sum of weighted mean square deviations of multi-resource requirements. A two-stage hybrid differential evolution particle swarm optimization algorithm is used to solve the model. In the first stage, differential evolution algorithm is used to produce new individuals, and in the second stage, particle swarm optimization algorithm uses a new speed update formula. In the first stage, in order to ensure that the optimal individual will not be destroyed by crossover and mutation, and to maintain the convergence of differential evolution algorithm, we try to introduce Elitist reservation (ER) strategy into differential evolution algorithm. In the second stage, we use a kind of Particle Swarm Optimization (PSO) algorithm with dynamic inertia weight. Through the dynamic change of inertia weight, the global search and local search ability of the algorithm can be adjusted flexibly. The case verification shows that the hybrid differential evolution particle swarm optimization algorithm can effectively solve the RLPMP model, and then effectively improve the balance of multi-project resources.

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Primiani, Rurik A., Kenneth H. Young, André Young, Nimesh Patel, Robert W. Wilson, Laura Vertatschitsch, Billie B. Chitwood, Ranjani Srinivasan, David MacMahon, and Jonathan Weintroub. "SWARM: A 32 GHz Correlator and VLBI Beamformer for the Submillimeter Array." Journal of Astronomical Instrumentation 05, no. 04 (December 2016): 1641006. http://dx.doi.org/10.1142/s2251171716410063.

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A 32[Formula: see text]GHz bandwidth VLBI capable correlator and phased array has been designed and deployed a at the Smithsonian Astrophysical Observatory’s Submillimeter Array (SMA). The SMA Wideband Astronomical ROACH2 Machine (SWARM) integrates two instruments: a correlator with 140[Formula: see text]kHz spectral resolution across its full 32[Formula: see text]GHz band, used for connected interferometric observations, and a phased array summer used when the SMA participates as a station in the Event Horizon Telescope (EHT) very long baseline interferometry (VLBI) array. For each SWARM quadrant, Reconfigurable Open Architecture Computing Hardware (ROACH2) units shared under open-source from the Collaboration for Astronomy Signal Processing and Electronics Research (CASPER) are equipped with a pair of ultra-fast analog-to-digital converters (ADCs), a field programmable gate array (FPGA) processor, and eight 10 Gigabit Ethernet (GbE) ports. A VLBI data recorder interface designated the SWARM digital back end, or SDBE, is implemented with a ninth ROACH2 per quadrant, feeding four Mark6 VLBI recorders with an aggregate recording rate of 64 Gbps. This paper describes the design and implementation of SWARM, as well as its deployment at SMA with reference to verification and science data.

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Borodin, Kirill, and Nurlan Zhangabayuly Zhangabay. "Mechanical characteristics, as well as physical-and-chemical properties of the slag-filled concretes, and investigation of the predictive power of the metaheuristic approach." Curved and Layered Structures 6, no. 1 (January 1, 2019): 236–44. http://dx.doi.org/10.1515/cls-2019-0020.

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AbstractOur article is devoted to development and verification of the metaheuristic optimisation algorithm in the course of selection of the compositional proportions of the slag-filled concretes. The experimental selection of various compositions and working modes, which ensure one and the same fixed level of a basic property, is the very labour-intensive and time-consuming process. This process cannot be feasible in practice in many situations, for example, in the cases, where it is necessary to investigate composite materials with equal indicators of resistance to aggressive environments or with equal share of voids in the certain range of dimensions. There are many similar articles in the scientific literature. Therefore, it is possible to make the conclusion on the topicality of the above-described problem. In our article, we will consider development of the methodology of the automated experimental-and-statistical determination of optimal compositions of the slag-filled concretes. In order to optimise search of local extremums of the complicated functions of the multi-factor analysis, we will utilise the metaheuristic optimisation algorithm, which is based on the concept of the swarm intelligence. Motivation in respect of utilisation of the swarm intelligence algorithm is conditioned by the absence of the educational pattern, within which it is not necessary to establish a certain pattern of learning as it is necessary to do in the neural-network algorithms. In the course of performance of this investigation, we propose this algorithm, as well as procedure of its verification on the basis of the immediate experimental verification.

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Hao, Li, Fan Xiangyu, and Shi Manhong. "Research on the Cooperative Passive Location of Moving Targets Based on Improved Particle Swarm Optimization." Drones 7, no. 4 (April 12, 2023): 264. http://dx.doi.org/10.3390/drones7040264.

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Aiming at the cooperative passive location of moving targets by UAV swarm, this paper constructs a passive location and tracking algorithm for a moving target based on the A optimization criterion and the improved particle swarm optimization (PSO) algorithm. Firstly, the localization method of cluster cooperative passive localization is selected and the measurement model is constructed. Then, the problem of improving passive location accuracy is transformed into the problem of obtaining more target information. From the perspective of information theory, using the A criterion as the optimization target, the passive localization process for static targets is further deduced. The Recursive Neural Network (RNN) is used to predict the probability distribution of the target’s location in the next moment so as to improve the localization method and make it suitable for the localization of moving targets. The particle swarm algorithm is improved by using grouping and time period strategy, and the algorithm flow of moving target location is constructed. Finally, through the simulation verification and algorithm comparison, the advantages of the algorithm in this paper are presented.

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Jadhav, Ashok Mohan, and K. Vadirajacharya. "Performance Verification of PID Controller in an Interconnected Power System Using Particle Swarm Optimization." Energy Procedia 14 (2012): 2075–80. http://dx.doi.org/10.1016/j.egypro.2011.12.1210.

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Fukuyama, Yoshikazu. "Verification of Dependability on Parallel Particle Swarm Optimization Based Voltage and Reactive Power Control." IFAC-PapersOnLine 48, no. 30 (2015): 167–72. http://dx.doi.org/10.1016/j.ifacol.2015.12.372.

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Liu, Yan, Qi Wang, Guoyi He, Li Zhang, and Jiao Wang. "A Novel Adaptive Particle Swarm Optimization Algorithm with Foraging Behavior in Optimization Design." MATEC Web of Conferences 166 (2018): 02004. http://dx.doi.org/10.1051/matecconf/201816602004.

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The method of repeated trial and proofreading is generally used to the convention reducer design, but these methods is low efficiency and the size of the reducer is often large. Aiming the problems, this paper presents an adaptive particle swarm optimization algorithm with foraging behavior, in this method, the bacterial foraging process is introduced into the adaptive particle swarm optimization algorithm, which can provide the function of particle chemotaxis, swarming, reproduction, elimination and dispersal, to improve the ability of local search and avoid premature behavior. By test verification through typical function and the application of the optimization design in the structure of the reducer with discrete and continuous variables, the results are shown that the new algorithm has the advantages of good reliability, strong searching ability and high accuracy. It can be used in engineering design, and has a strong applicability.

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Liu, Yi Lei, Dong Gao, and Gang Wei Cui. "Volumetric Error Model of Large CNC Machine Tool and Verification Based on Particle Swarm Optimization." Key Engineering Materials 579-580 (September 2013): 76–79. http://dx.doi.org/10.4028/www.scientific.net/kem.579-580.76.

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Volumetric error has large effect on machine tool accuracy; improving CNC machine tool accuracy through error compensation has received significant attention recently. This paper intends to represent volumetric error measurement based on laser tracker. The volumetric error is modeled by homogenous transformation matrix with each coordinate corresponding to each motion axis. Based on parts of spatial points volumetric error, the geometric errors which affect volumetric positioning error are verified through particle swarm optimization with the L2 parameters as the target function. The chebyshev orthogonal polynomials are applied to approximate geometric errors.

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Chen, Chuandong, Rongshan Wei, Shaohao Wang, and Wei Hu. "Novel Verification Method for Timing Optimization Based on DPSO." VLSI Design 2018 (March 21, 2018): 1–8. http://dx.doi.org/10.1155/2018/8258397.

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Timing optimization for logic circuits is one of the key steps in logic synthesis. Extant research data are mainly proposed based on various intelligence algorithms. Hence, they are neither comparable with timing optimization data collected by the mainstream electronic design automation (EDA) tool nor able to verify the superiority of intelligence algorithms to the EDA tool in terms of optimization ability. To address these shortcomings, a novel verification method is proposed in this study. First, a discrete particle swarm optimization (DPSO) algorithm was applied to optimize the timing of the mixed polarity Reed-Muller (MPRM) logic circuit. Second, the Design Compiler (DC) algorithm was used to optimize the timing of the same MPRM logic circuit through special settings and constraints. Finally, the timing optimization results of the two algorithms were compared based on MCNC benchmark circuits. The timing optimization results obtained using DPSO are compared with those obtained from DC, and DPSO demonstrates an average reduction of 9.7% in the timing delays of critical paths for a number of MCNC benchmark circuits. The proposed verification method directly ascertains whether the intelligence algorithm has a better timing optimization ability than DC.

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Huang, Fangchen, Yuhan Chen, and Wenzhou Lu. "Multi-objective Optimal Scheduling of Small Integrated Energy System Based on Improved Particle Swarm Optimization Algorithm." Journal of Physics: Conference Series 2400, no. 1 (December 1, 2022): 012051. http://dx.doi.org/10.1088/1742-6596/2400/1/012051.

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Abstract In order to explore a low-carbon and economical way of supplying energy for buildings and promote the transformation of the energy system, a small integrated energy system including energy supply equipment, energy conversion equipment and energy storage equipment is designed. After comprehensively considering operating cost and carbon emission, a multi-objective optimal scheduling model of the system is constructed. A genetic particle swarm optimization algorithm based on random dynamic inertia weight is proposed to solve the model and analyze the output of each piece of equipment in the system under the optimal operating strategy. An example of a building in a region with hot summer and cold winter is introduced for verification, and the simulation results show that the proposed small integrated energy system model is economical and environmentally-friendly, and the improved particle swarm optimization algorithm has a better optimization effect.

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Mahalakshmi, S., and A. Arokiasamy. "A new particle swarm optimisation variant-based experimental verification of an industrial robot trajectory planning." International Journal of Operational Research 41, no. 3 (2021): 399. http://dx.doi.org/10.1504/ijor.2021.116250.

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Sengupta, A., S. Mukhopadhyay, and A. K. Sinha. "Automated Verification of Power System Protection Schemes—Part II: Test Case Generation Using Swarm Intelligence." IEEE Transactions on Power Delivery 30, no. 5 (October 2015): 2087–95. http://dx.doi.org/10.1109/tpwrd.2014.2376592.

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S, Mahalakshmi, and A. Arokiasamy. "A new Particle Swarm Optimization variant based experimental verification of an industrial robot trajectory planning." International Journal of Operational Research 1, no. 1 (2021): 1. http://dx.doi.org/10.1504/ijor.2021.10019632.

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Raghavendra, R., Bernadette Dorizzi, Ashok Rao, and G. Hemantha Kumar. "Particle swarm optimization based fusion of near infrared and visible images for improved face verification." Pattern Recognition 44, no. 2 (February 2011): 401–11. http://dx.doi.org/10.1016/j.patcog.2010.08.006.

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Czejdo, Bogdan, Wiktor Daszczuk, Waldemar Grabski, and Sambit Bhattacharya. "Cooperation of multiple autonomous robots and analysis of their swarm behavior." AUTOBUSY – Technika, Eksploatacja, Systemy Transportowe 19, no. 12 (December 31, 2018): 872–79. http://dx.doi.org/10.24136/atest.2018.516.

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In this paper, we extended previous studies of cooperating autonomous robots to include situations when environmental changes and changes in the number of robots in the swarm can affect the efficiency to execute tasks assigned to the swarm of robots. We have presented a novel approach based on partition of the robot behaviour. The sub-diagrams describing sub-routs allowed us to model advanced interactions between autonomous robots using limited number of state combinations avoiding combinatorial explosion of reachability. We identified the systems for which we can ensure the correctness of robots interactions. New techniques were presented to verify and analyze combined robots’ behaviour. The partitioned diagrams allowed us to model advanced interactions between autonomous robots and detect irregularities such as deadlocks, lack of termination etc. The techniques were presented to verify and analyze combined robots’ behaviour using model checking approach. The described system, Dedan verifier, is still under development. In the near future, timed and probabilistic verification are planned..

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Zhong, Zhifeng, Chenxi Yang, Wenyang Cao, and Chenyang Yan. "Short-Term Photovoltaic Power Generation Forecasting Based on Multivariable Grey Theory Model with Parameter Optimization." Mathematical Problems in Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/5812394.

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Owing to the environment, temperature, and so forth, photovoltaic power generation volume is always fluctuating and subsequently impacts power grid planning and operation seriously. Therefore, it is of great importance to make accurate prediction of the power generation of photovoltaic (PV) system in advance. In order to improve the prediction accuracy, in this paper, a novel particle swarm optimization algorithm based multivariable grey theory model is proposed for short-term photovoltaic power generation volume forecasting. It is highlighted that, by integrating particle swarm optimization algorithm, the prediction accuracy of grey theory model is expected to be highly improved. In addition, large amounts of real data from two separate power stations in China are being employed for model verification. The experimental results indicate that, compared with the conventional grey model, the mean relative error in the proposed model has been reduced from 7.14% to 3.53%. The real practice demonstrates that the proposed optimization model outperforms the conventional grey model from both theoretical and practical perspectives.

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Gao, Yanbin, Lianwu Guan, and Tingjun Wang. "Triaxial Accelerometer Error Coefficients Identification with a Novel Artificial Fish Swarm Algorithm." Journal of Sensors 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/509143.

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Artificial fish swarm algorithm (AFSA) is one of the state-of-the-art swarm intelligence techniques, which is widely utilized for optimization purposes. Triaxial accelerometer error coefficients are relatively unstable with the environmental disturbances and aging of the instrument. Therefore, identifying triaxial accelerometer error coefficients accurately and being with lower costs are of great importance to improve the overall performance of triaxial accelerometer-based strapdown inertial navigation system (SINS). In this study, a novel artificial fish swarm algorithm (NAFSA) that eliminated the demerits (lack of using artificial fishes’ previous experiences, lack of existing balance between exploration and exploitation, and high computational cost) of AFSA is introduced at first. In NAFSA, functional behaviors and overall procedure of AFSA have been improved with some parameters variations. Second, a hybrid accelerometer error coefficients identification algorithm has been proposed based on NAFSA and Monte Carlo simulation (MCS) approaches. This combination leads to maximum utilization of the involved approaches for triaxial accelerometer error coefficients identification. Furthermore, the NAFSA-identified coefficients are testified with 24-position verification experiment and triaxial accelerometer-based SINS navigation experiment. The priorities of MCS-NAFSA are compared with that of conventional calibration method and optimal AFSA. Finally, both experiments results demonstrate high efficiency of MCS-NAFSA on triaxial accelerometer error coefficients identification.

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Liu, Huaimin, Xiangjiang Wang, and Meng Li. "External force estimation for robotic manipulator base on particle swarm optimization." International Journal of Advanced Robotic Systems 18, no. 6 (November 1, 2021): 172988142110637. http://dx.doi.org/10.1177/17298814211063744.

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The safe disposal of nuclear waste in radioactive environment urgently needs cost-effective approaches. Toward this goal, this article developed a method to external force estimation based on the identified model without force sensors. Firstly, the mathematical model including joint friction was obtained and transformed into the linear combination of unknown parameter to be estimated. Secondly, the unknown parameters were identified based on the improved particle swarm optimization algorithm, the identification procedure was implemented by optimizing the excitation trajectories to excite joint motion and sampling relevant data. Identified results were compared with the biogeography-based optimization algorithm and the cuckoo search algorithm. Then, the identified dynamic parameter was applied to external force estimation. Finally, the verification of external force estimation has been carried out using the Kinova Jaco2 robot manipulator, and the experimental results showed that the external forces by the proposed method could be estimated with an root mean square error of 0.7 N.

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Zhang, Kui, and Shan Zhu. "Defect Detection Image Processing Technology Based on Swarm Intelligence Optimization Algorithm." Journal of Physics: Conference Series 2400, no. 1 (December 1, 2022): 012031. http://dx.doi.org/10.1088/1742-6596/2400/1/012031.

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Abstract The swarm intelligence optimization algorithm has obtained good results in practical application in the field of image processing with defect detection, and it has become the focus and hot spot of attention and research in the field of image processing. In this paper, the application of ALO as the representative of the relevant swarm intelligence optimization algorithm is studied to address the problems and shortcomings of image processing technology in the field of object defect detection. By extracting typical defect detection image samples, the effect of the application of the algorithm in sample processing is systematically studied. In addition, the introduction of perturbation strategy and inertia weights in ALO effectively improves the search performance of the algorithm. Finally, this paper analyzes the performance comparison between the commonly used defect detection image processing techniques and the algorithm in this paper by establishing comparative verification experiments. The experimental results show that the image processing strategy constructed in this paper has significant application advantages in the dimensions of image enhancement and image processing applicability.

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Matsuzaki, Ryosuke, Kentaro Yamamoto, and Akira Todoroki. "Delamination detection in carbon fiber reinforced plastic cross-ply laminates using crack swarm inspection: Experimental verification." Composite Structures 173 (August 2017): 127–35. http://dx.doi.org/10.1016/j.compstruct.2017.04.014.

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Lu, Jie, Weidong Guo, Jinpei Liu, Ruijie Zhao, Yueyang Ding, and Shaoshuai Shi. "An Intelligent Advanced Classification Method for Tunnel-Surrounding Rock Mass Based on the Particle Swarm Optimization Least Squares Support Vector Machine." Applied Sciences 13, no. 4 (February 5, 2023): 2068. http://dx.doi.org/10.3390/app13042068.

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The fast and accurate classification of surrounding rock mass is the basis for tunnel design and construction and has significant value in engineering applications. Therefore, this paper proposes a method for classifying and predicting surrounding rock mass based on particle swarm optimization (PSO)–least squares support vector machine (LSSVM). The premise of the research is that the data acquired from digital drilling technology are divided into a training group and a test group; the training group continuously optimizes the algorithm for the particle swarm optimization least squares support vector machine, and then the test group is used for verification. Moreover, the fast searching abilities of the particle swarm significantly accelerate the computational power and computational accuracy of the least squares support vector machine, making it a high-speed analog search tool. Taking the Jiaozhou Bay undersea tunnel in China as an example, a comparison of the evaluation results of PSO-LSSVM and QGA-RBF (quantum genetic algorithm-radical basis function neural network) is undertaken. The results show that PSO-LSSVM matches well with the field-measured surrounding rock grade. Applying the method in an engineering context proves that it has good self-learning abilities, even when the sample size is small and the prediction accuracy is high; as such, it meets the engineering requirements. The technique has the advantages of small sample prediction, pattern recognition, and nonlinear prediction.

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Journal articles on the topic 'Swarm verification'

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