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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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Model Updating for Solar Array Based on the Improved ga-pso Hybrid algorithm

Model Updating for Solar Array Based on the Improved GA-PSO ...

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中国通信学会第六届学术年会

作者： KONG Xian-ten QIN Yu-ling LUO Wen-bo Research Center of Satellite Technology Harbin Institute of TechnologyHarbin150001China China Academy of Space Technology Beijing100086China

The solar array whose quality has great influence on the work efficiency of the spacecraft is the most important power supply system and the honeycomb sandwich panel which has high specific strength and stiffness is the perfect material for solar *** finite element model(FEM) of the honeycomb sandwich panel includes idealized hypothesis and simplification,which results in the difference between the calculating data and the test *** this paper the honeycomb sandwich panel is equivalent to a structure which can be calculated by NASTRAN based on the equivalence theory and then the improved Genetic-Partical Swarm Optimization ( ga-pso) algorithm is used to update the FEM,the updated precision and efficiency of the ga,pso and ga-pso are compared and the results show that these random search algorithms can update high order modes even use the incomplete low order *** improved ga-pso algorithm can use ga to efficiently search for the global optimal solution at the early stage,and use pso to intensively search for the local optimal solution at the later stage,it has higher searching efficiency and precision,the updated model has good apprcaching and prediction ability and can be taken as the fiducial model for the following calculation.

关键词： spacecraft finite element model model updating solar array ga-pso algorithm global-optimal solution

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Model Updating for Solar Array Based on the Improved ga-pso Hybrid algorithm

Model Updating for Solar Array Based on the Improved GA-PSO ...

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促进网络融合，服务经济社会发展——中国通信学会第六届学术年会

作者： KONG Xian-ren QIN Yu-ling LUO Wen-bo Research Center of Satellite Technology Harbin Institute of Technology China Academy of Space Technology

The solar array whose quality has great influence on the work efficiency of the spacecraft is the most important power supply system and the honeycomb sandwich panel which has high specific strength and stiffness is the perfect material for solar *** finite element model(FEM) of the honeycomb sandwich panel includes idealized hypothesis and simplification,which results in the difference between the calculating data and the test *** this paper the honeycomb sandwich panel is equivalent to a structure which can be calculated by NASTRAN based on the equivalence theory and then the improved Genetic- Partical Swarm Optimization(ga-FSO) algorithm is used to update the FEM,the updated precision and efficiency of the ga,pso and ga- pso are compared and the results show that these random search algorithms can update high order modes even use the incomplete low order *** improved ga-pso algorithm can use ga to efficiently search for the global optimal solution at the early stage,and use pso to intensively search for the local optimal solution at the later stage,it has higher searching efficiency and precision,the updated model has good approaching and prediction ability and can be taken as the fiducial model for the following calculation.

关键词： spacecraft finite element model model updating solar array ga-pso algorithm global-optimal solution

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A learning algorithm of artificial neural network based on ga - pso

A learning algorithm of artificial neural network based on G...

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6th World Congress on Intelligent Control and Automation

作者： Du, Shiqiang Li, Wanshe Cao, Kai Shaanxi Normal Univ Coll Math & Informat Sci Xian 710062 Peoples R China

ISBN: (纸本)1424403316

In order to get over the insufficiency of back-propagation (BP) algorithm, after analyses of genetic algorithm (ga) and particle swarm optimization (pso), a ga-pso algorithm is proposed. In ga-pso, individuals in a new generation are created, not only by crossover and mutation operation in ga but also by pso, based on redefine local optimization swarm. So it can both avoid local minimum and has good global search capacity. The performance of ga-pso is compared to both ga and pso in artificial neural networks weight training, demonstrating its superiority.

关键词： BP algorithm genetic algorithm particle swarm optimization ga-pso algorithm

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Research on multi-load AGV path planning of weaving workshop based on time priority

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2019年第4期16卷 2277-2292页

作者： Du, Li-zhen Ke, Shanfu Wang, Zhen Tao, Jing Yu, Lianqing Li, Hongjun Wuhan Text Univ Sch Mech Engn & Automat Wuhan 430200 Hubei Peoples R China Hubei Polytech Univ Sch Mech & Elect Engn Huangshi 435003 Hubei Peoples R China

The multi-load AGV (Automatic Guided Vehicle) is a new kind of materials handling equipment used to load cloth automatically in an intelligent weaving workshop. It can transport multiple rolls of cloth and choose the correct, most effective path to improve the transportation efficiency without people engaged in. This paper creates a feasible path topology according to the layout of the workshop and the logistics environment, and uses the Warshall-Floyd algorithm to search for the optimal route between two arbitrary points. The aim of the path planning is to maximize the machine efficiency, which is constrained by environmental limits, load limits and work limits. This paper establishes the mathematical model of the path planning problem using the mixed genetic particle swarm optimization algorithm (ga-pso) to solve the problem, and the particle iteration mechanism based on the time priority is proposed to make the evolution more directional and accelerate the convergence speed of the algorithm. The effectiveness and practicability of the model and methods are verified by simulation and benefit analysis.

关键词： multi-load AGV intelligent weaving workshop time priority path planning ga-pso algorithm

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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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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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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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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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