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ga-pso algorithm for Microseismic Source Location

APPLIED SCIENCES-BASEL 2025年第4期15卷 1841-1841页

作者： Han, Yaning Zeng, Fanyu Fu, Liangbin Zheng, Fan Jilin Univ Coll Instrumentat & Elect Engn Key Lab Geophys Explorat Equipment Minist Educ Changchun 130026 Peoples R China

Accurate source location is a critical component of microseismic monitoring and early warning systems. To improve the accuracy of microseismic source location, this manuscript proposes a ga-pso algorithm that combines the Genetic algorithm (ga) with Particle Swarm Optimization (pso). The ga-pso algorithm enhances the pso algorithm by dynamically adjusting the balance between global exploration and local exploitation through a sinusoidal function for the nonlinear adjustment of both learning factors, and an adaptive inertia weight that decreases quadratically with iterations. Additionally, the precision of the solutions is further improved through the crossover and mutation operations of the ga. In the simulated location model, the ga-pso algorithm demonstrated the smallest error value, outperforming both the ga and pso algorithm in terms of accuracy. Furthermore, the ga-pso algorithm exhibited minimal sensitivity to wave speed fluctuations of +/- 1%, +/- 3%, and +/- 5%, maintaining the error within 0.5 m. The validation through the blasting experiment at the Shizhuyuan mine further confirmed the enhanced accuracy of the ga-pso algorithm, with a location error of 20.08 m, representing an improvement of 59% over the ga and 43% over the pso algorithm.

关键词： microseismic source location ga-pso algorithm dynamic learning factor adaptive inertia weight

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A Resource Allocation Problem Based on ga-pso algorithm

A Resource Allocation Problem Based on GA-PSO Algorithm

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2016 3rd International Conference on Materials Engineering,Manufacturing Technology and Control(ICMEMTC 2016)

作者： Guo Zheyu North China Electric Power University Baoding

This paper proposes a scheme for resource allocation of taxi. Using SOM neural network divides the area into several areas, and calculating taxi transfer number for each area by using a method of dynamic subsidy and ga-pso algorithm. Before and after comparing with the scheme of the taxi demand matching degree, and we find the matching degree has been greatly improved finally.

关键词： SOM neural network ga-pso algorithm dynamic subsidy

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Improved fractional-order hysteresis-equivalent circuit modeling for the online adaptive high-precision state of charge prediction of urban-electric-bus lithium-ion batteries

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INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS 2024年第1期52卷 420-438页

作者： Zeng, Jiawei Wang, Shunli Cao, Wen Zhang, Mengyun Fernandez, Carlos Guerrero, Josep M. M. Southwest Univ Sci & Technol Sch Informat Engn Mianyang 621010 Peoples R China Robert Gordon Univ Sch Pharm & Life Sci Aberdeen Scotland Aalborg Univ Dept Energy Technol Aalborg Denmark

Accurate state of charge (SOC) estimation is based on a precise battery model and is the focus of the battery management system (BMS). First, based on the second-order RC equivalent circuit model and Grunwald-Letnikov (G-L) definition, the high-precision fractional-order hysteresis-equivalent circuit model (FH-ECM) is established considering the open-circuit voltage hysteresis effect. Then, the global parameters of the battery model are identified using a particle swarm algorithm optimized by the genetic algorithm (ga-pso). Third, a fractional-order adaptive unscented Kalman filter (FOAUKF) algorithm is derived to estimate the SOC of lithium-ion batteries. Finally, the feasibility of the model and algorithm is verified under complex working conditions. Under the dynamic stress test (DST) condition, the accuracy of model terminal voltage has been improved by 37.83%, and the error of SOC estimation has been reduced by 11.28%. Under Beijing bus dynamic stress test (BBDST) condition, the model terminal voltage accuracy has been improved by 51.44%, and the SOC estimation error has been reduced by 35.71%. The experimental results fully confirm the accuracy of the fractional-order hysteresis-equivalent circuit modeling method.

关键词： Beijing bus dynamic stress test fractional-order adaptive unscented Kalman filter fractional-order hysteresis model ga-pso algorithm lithium-ion batteries

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Optimization of process parameters of ultrasonic-assisted end face grinding of Cf/SiC composites

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JOURNAL OF THE AMERICAN CERAMIC SOCIETY 2025年第8期108卷

作者： Bao, Yan Liu, Hanbin Wang, Zhongwang Lu, Jiawei Kang, Renke Sun, Jiansong Dalian Univ Technol State Key Lab High Performance Precis Mfg Dalian Peoples R China

Carbon fiber-reinforced ceramic matrix (C-f/SiC) composites are featured with high strength, high-temperature resistant, and low density, and become an important candidate thermal structural material for the hot end components of high-performance aero-engines. Due to their properties of hardness, brittleness, inhomogeneity, and anisotropy, ultrasonic-assisted grinding is a commonly used method to process C-f/SiC composites. The complex nonlinear mapping relationship between ultrasonic-assisted grinding machining parameters and machining quality of C-f/SiC composites needs to be clarified. The Taguchi L16 orthogonal array method was used to carry out ultrasonic-assisted end grinding experiment of C-f/SiC composites in the paper. Combined with the response surface method, the prediction models of surface roughness and grinding force were established, and the prediction of the models was more than 95%. The experimental results were analyzed by signal-to-noise ratio analysis, variance analysis, and response surface method. The effects of spindle speed, feed rate, grinding depth, and ultrasonic amplitude on grinding force and surface roughness were investigated. The influence of machining parameters on grinding force and surface roughness was obtained. Finally, the genetic-particle swarm algorithm was used to optimize the prediction models under different grinding force weights, respectively, and the optimal machining parameters were obtained.

关键词： C-f/SiC composites ga-pso algorithm parameters optimization prediction model ultrasonic-assisted end grinding

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Optimization Model of the Cold Rolling Roll Grinding Volume Based on Nonlinear Fatigue Accumulation Regulation

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JOURNAL OF FAILURE ANALYSIS AND PREVENTION 2023年第5期23卷 2131-2141页

作者： Sun, Wenquan Yuan, Tieheng Wu, Zhangyu He, Anrui Liu, Yang Zheng, Shuaishuai Univ Sci & Technol Beijing Natl Engn Res Ctr Adv Rolling & Intelligent Mfg Beijing Peoples R China

The main form of roll failure in cold rolling production is fatigue damage. To avoid industrial production accidents, the roll grinding amount after service is usually manually determined in accordance with empirical settings. This leads to excessive grinding. To address this problem, this work takes a 2130-mm UCM cold rolling production line as an example and establishes an elastic deformation model of the mill roll system based on the influence function method. The three-way stress and maximum tangential stress distribution inside the roll are calculated by means of the Hertz formula. Combined with the nonlinear fatigue accumulation model, a roll fatigue prediction model is proposed to quantitatively describe the roll fatigue state. According to the roll fatigue distribution data and actual production situation, the roll fatigue curve is shifted superimposed, and the roll grinding optimization model is established using a genetic particle swarm algorithm. It can meet the requirements of industrial production in terms of convergence speed and optimization. It applies the model to a cold rolling production line in a factory. Field application shows that the model can be used to quantify and detect roll fatigue in real time. The rolls can be dismounted at the right time, and the production accident rate from fatigue can be reduced by 33.3%. The consumption of backup rolls, intermediate rolls and work rolls can be reduced by 23.2, 24.1 and 28.6% each year, respectively. The application of this model can greatly reduce the production cost for a plant, ensure production safety and extend the service life of rolls.

关键词： Roll fatigue Interroll contact pressure Nonlinear fatigue accumulation ga-pso algorithm Roll grinding

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The identifiability of gene regulatory networks: the role of observation data

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JOURNAL OF BIOLOGICAL PHYSICS 2022年第1期48卷 93-110页

作者： Huang, Xiao-Na Shi, Wen-Jia Zhou, Zuo Zhang, Xue-Jun HeXi Univ Sch Phys & Electromech Engn Zhangye Gansu Peoples R China Xian Univ Technol Xian Shaanxi Peoples R China

Identifying gene regulatory networks (GRN) from observation data is significant to understand biological systems. Conventional studies focus on improving the performance of identification algorithms. However, besides algorithm performance, the GRN identification is strongly depended on the observation data. In this work, for three GRN S-system models, three observation data collection schemes are used to perform the identifiability test procedure. A modified genetic algorithm-particle swarm optimization algorithm is proposed to implement this task, including the multi-level mutation operation and velocity limitation strategy. The results show that, in scheme 1 (starting from a special initial condition), the GRN systems are of identifiability using the sufficient transient observation data. In scheme 2, the observation data are short of sufficient system dynamic. The GRN systems are not of identifiability even though the state trajectories can be reproduced. As a special case of scheme 2, i.e., the steady-state observation data, the equilibrium point analysis is given to explain why it is infeasible for GRN identification. In schemes 1 and 2, the observation data are obtained from zero-input GRN systems, which will evolve to the steady state at last. The sufficient transient observation data in scheme 1 can be obtained by changing the experimental conditions. Additionally, the valid observation data can be also obtained by means of adding impulse excitation signal into GRN systems (scheme 3). Consequently, the GRN systems are identifiable using scheme 3. Owing to its universality and simplicity, these results provide a guide for biologists to collect valid observation data for identifying GRNs and to further understand GRN dynamics.

关键词： Identifiability Observation data Gene regulatory networks ga-pso algorithm

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Numerical investigation of stability of deep excavations supported by soil-nailing method

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GEOMECHANICS AND GEOENGINEERING-AN INTERNATIONAL JOURNAL 2021年第6期16卷 434-451页

作者： Pak, Ali Maleki, Jafar Aghakhani, Nima Yousefi, Mojtaba Sharif Univ Technol Dept Civil Engn Tehran Iran

Deep excavation in urban areas can cause instability problems due to significant settlement at the ground surface and large movements at the excavation facing walls. One of the most popular methods used to stabilise these excavations is utilising soil-nailing method. This method has also been widely used to stabilise natural slopes and earth retaining structures. Because of the complexity involved in the mechanism of this stabilising system due to interacting effects of the soil, nails, grout and shotcrete, numerical modelling with high accuracy should be used to analyse the behaviour of the soil-nailed walls. Considering all aspects of soil-structure interaction in the present research, a large number of parametric studies are carried out to investigate the stability of deep excavations with different geometries, and the results are shown in the form of design charts and tables. Also, by employing the results of the numerical simulations and using a meta-heuristic algorithm, a simplified equation has been developed to predict the deflections of deep excavations in order to increase the safety measures during the construction and stabilisation of the excavation.

关键词： Deep excavation nailing wall deflection parametric study safety factor ga-pso algorithm

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A comprehensive FE study for design of anchored wall systems for deep excavations

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2022年 122卷 104340-104340页

作者： Maleki, Jafar Pak, Ali Yousefi, Mojtaba Aghakhani, Nima Sharif Univ Technol Dept Civil Eng Geotech Eng Tehran Iran Sharif Univ Technol Dept Civil Eng Tehran Iran Sharif Univ Technol Dept Comp Eng Tehran Iran

Anchored wall system is one of the common methods used for deep excavation stabilization adjacent to sensitive structures in urban areas. A key aspect of the stability analysis of deep excavations is the amount of deformations occurring on the facing wall and the adjacent structures. In this research, a large number of parametric studies considering all aspects of soil-structure interaction is carried out for different excavation geometries to find the optimal design, and the outcome is shown in the form of design tables and charts. Also, by a ga-pso algorithm and using the large database obtained from the numerical simulations, a simple equation is developed that can predict the deflections caused by deep excavations. The results of the proposed equation demonstrate high accuracy and marked consistency with the algorithm results, numerical analysis outcome, and real cases data.

关键词： Deep excavation Anchored wall system Wall deformation ga-pso algorithm

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Evaluating the prospect of utilizing excess energy and creating employments from a hybrid energy system meeting electricity and freshwater demands using multi-objective evolutionary algorithms

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ENERGY 2022年第PartB期238卷 121860-121860页

作者： Das, Pronob Das, Barun K. Rahman, Mushfiqur Hassan, Rakibul Rajshahi Univ Engn & Technol Dept Mech Engn Rajshahi 6204 Bangladesh Rahimafrooz Batteries Ltd Dept Assembly Dhaka Bangladesh

Hybrid renewable energy systems have been accepted as one of the most convenient approaches towards the green energy future. As the renewable penetration propagates throughout the world, benefits of those systems other than providing clean energy have been brought to light. When hybridized, the stand-alone systems tend to generate significant energy excess to demand which needs to be disposed. These two aspects have been addressed in this study. Hybrid renewable energy systems containing solar photovoltaic, wind turbine, diesel generator and battery are optimized to supply reliable electricity and fresh water to a community of an Island not connected to the national grid in Bangladesh. The generated excess energy of the stand-alone hybrid system is utilized to run a reverse osmosis process for the desalination of sea water. Job creation, one of the prospective benefits of renewables, is maximized while minimizing cost and excess energy. The systems are optimized using genetic algorithm, particle swarm optimization and a hybrid of these two algorithms. The results reveal that, the hybrid algorithm exhibits better convergence and provides the optimum system with a cost of energy of 0.234 $/kWh, 1.64 jobs and 24,038 kWh/yr excess energy. By utilizing excess energy, a maximum of 11.8% and 9.3% reduction is achieved in overall system cost and electricity cost, respectively while job creation increases by 6%. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Hybrid system Optimization ga-pso algorithm Utilization of excess energy Job creation

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Double-objective optimization of electro-thermal (E-T) micro-actuator for fiber switch

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JOURNAL OF MICROMECHANICS AND MICROENGINEERING 2021年第4期31卷

作者： Chen, Hao Wang, Xin-jie Cao, Yun Wang, Jiong Xi, Zhan-wen Nie, Wei-rong Nanjing Univ Sci & Technol Sch Mech Engn Nanjing Jiangsu Peoples R China

In this paper, a novel optimization method, in which composed of the genetic algorithm, particle swarm optimization (ga-pso) and improved gradient descent algorithm, are used to conduct a double-objective optimization for the U-shaped actuator. In the procedure of optimization, two objectives, i.e. force and displacement, and four main sizes are utilized. Before, the deep reactive-ion etch (DRIE) technology is applied to the fabrication of the U-shaped actuator. When different voltages are applied on the actuator, the displacement obtained from numerical calculation always shows a good agreement with that from experiment by edge detection algorithm. Similar phenomenon can be also seen when an external force supplied by the nanoindentation system FemtoTools in the experiment is loaded on the actuator. Based on the validated simulation model of the U-shaped actuator, the improved gradient descent method ensures its displacement very close to 50 mu m (target displacement) while the ga-pso algorithm is used to maximize the output force. In this procedure, the hybrid optimization method implemented by Matlab is incorporated into ANSYS simulation. Preliminary analysis shows that the displacement and force of the particles in each iteration concentrate together with the iteration growing. Fine convergence, whose velocity only depends on the number of particle in the algorithm, is also found in each optimization. Furthermore, the optimized actuators have homologous value of the size variables. At 15 V voltage, the displacement and largest output force of the U-shaped actuator are 2 mN and 50.1 mu m, respectively. Finally, an actuator with 30% improvement of the output force is obtained when the displacement condition is meet. According to the optimization result and further parametric scanning simulation analysis, the design range and fabrication error of the sizes of the U-shaped actuator are obtained.

关键词： micro-actuator double-objective optimization ga-pso algorithm improved gradient descent method

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