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particle swarm optimisation algorithm for crack shape reconstruction in magnetic flux leakage nondestructive testing

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NONDESTRUCTIVE TESTING AND EVALUATION 2009年第1-2期24卷 243-250页

作者： Zhang, Yong Ye, Zhongfu Hefei Univ Technol Sch Comp & Informat Hefei Peoples R China Univ Sci & Technol China Inst Stat Signal Proc Hefei Peoples R China Hefei Univ Technol Sch Comp & Informat Hefei Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China

Magnetic flux leakage testing is widely used to examine ferromagnetic materials. Considering the importance of estimating the size of surface crack in metals, which is used in nuclear power, railway and piping industries, a method based on particle swarm optimisation algorithm is proposed in this article. This approach maps the size of crack to the location of particles. After initialising the locations and velocities of the particle population, it evaluates the fitness value of the individual particle and tracks the optimal one. It then updates the positions and velocities of particles with the previous fitness values and repeats these steps until the best possible result is obtained. The fitness value is obtained by summing the absolute measurement error of the magnetic leakage field intensity of the crack. The results of simulation experiments demonstrate the effectiveness of the proposed method.

关键词： magnetic flux leakage crack shape reconstruction particle swarm optimisation algorithm magnetic dipole model

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Determine the most effective process control parameters on activated sludge based on particle swarm optimisation algorithm (Case Study: South wastewater treatment plant of Tehran)

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL ANALYTICAL CHEMISTRY 2022年第19期102卷 7595-7616页

作者： Rafati, Mina Pazouki, Mohammad Ghadamian, Hossein Hossein Nia, Azarmidokht Jalilzadeh, Ali Mat & Energy Res Ctr Res Dept Energy Karaj Iran Arman Tadbir Palayesh Consulting Engineer Co Tehran Iran

Wastewater treatment plants (WWTPs) are major energy consumers and cause environmental impact on receiving waters. Many WWTPs are operated in a less-than-optimal manner with respect to both treatment and energy efficiency. A better solution is to optimize the operation and processes of the existing WWTPs. Therefore, the most effective process parameter for providing control strategies was *** South WWTP of Tehran was designed and simulated based on the activated sludge model using MATLAB/ Simulink software for the first time in the country to obtain the factors affecting WWTP. To calibrate this model some kinetic and stoichiometric coefficients were determined. Then the simulator validation was performed by three error calculation methods. Finally, the values of three main controllers, including the optimum rate or return activated sludge (RAS), internal recycle (IR) and oxygen transfer coefficient (KLa) are determined using the particle swarm Optimization algorithm. According to the results, coefficients such as Y, kd, K and KS for Oxidation-Ditch process were in the range of 0.303-0.331mgVSS /mg sCOD, 0.030-0.033 1/day, 1.65-1.93 1/day and 37.6-44.92 mg sCOD/l, respectively and the Mean of COD, BOD5, TSS and TN removal was obtained 94.8 +/- 0.4, 97.3 +/- 0.65, 94.7 +/- 1.5 and 56 +/- 7.46, respectively. Also, the percentage of Root Mean Square for TN, TSS, COD, BOD5 was 3.14%, 2.95%, 3.13% and 5.2%, respectively, Pearson correlation coefficient was 0.88, 0.93, 0.89, 0.99 and absolute mean error of 2.62%, 2.47%, 2.6% and 3.58%, respectively, which shows that the simulation outputs are compatible with the effluent of plant. To achieve the best process performance conditions, RAS and IR must be considered for 1.7 and 0.8 percent of influent wastewater and KLa is suggested to be 154 d(-1). Therefore, by using the optimization performed, the effect of other controllers on the process can be investigated and selected.

关键词： Simulation activated Sludge Model No. 1 internal Recycle Flow return Activated Sludge oxygen transfer coefficient particle swarm optimisation algorithm

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Optimal capacity allocation of standalone wind/solar/battery hybrid power system based on improved particle swarm optimisation algorithm

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IET RENEWABLE POWER GENERATION 2013年第5期7卷 443-448页

作者： Wang, Jidong Yang, Fan Tianjin Univ Minist Educ Key Lab Smart Grid Tianjin 300072 Peoples R China Tianjin Univ Sch Elect Engn & Automat Tianjin 300072 Peoples R China

A standalone wind/solar/battery hybrid power system, making full use of the nature complementarity between wind and solar energy, has an extensive application prospect among various newly developed energy technologies. The capacity of the hybrid power system needs to be optimised in order to make a tradeoff between power reliability and cost. In this study, each part of the wind/solar/battery hybrid power system is analysed in detail and an objective function combining total owning cost and loss of power supply probability is built. To solve the problems with non-linearity, complexity and huge computation, an improved particle swarm optimisation (PSO) algorithm is developed, which integrates the taboo list to broaden the search range and introduces 'restart' and 'disturbance' operation to enhance the global searching capability. The simulation results indicate that the proposed algorithm is more stable and provides better results in solving the optimal allocation of the capacity of the standalone wind/solar/battery hybrid power system compared with the standard PSO algorithm.

关键词： battery storage plants hybrid power systems particle swarm optimisation power generation economics power generation reliability solar power stations wind power plants optimal capacity allocation standalone wind-solar-battery hybrid power system particle swarm optimisation algorithm wind energy solar energy power reliability power cost power supply probability PSO algorithm

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The forecasting residual life of underground pipeline based on particle swarm optimisation algorithm

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2009年第4期1卷 270-275页

作者： Liu, Qin Ming Lv, Wenyuan Shanghai Univ Sci & Technol Coll Management Shanghai 200093 Peoples R China

Grey theory explores the use of the PSO algorithm and this paper analysed the feasibility of using this algorithm to forecast the residual life of underground pipeline. It is allowed to offer fewer data while using this method to forecast its residual life. Example shows that the method of the PSO algorithm based on grey theory optimisation is superior to the existing forecasting methods such as the grey forecasting method and grey theory based on the GA optimisation method.

关键词： underground pipeline residual life grey theory particle swarm optimisation algorithm PSO algorithm

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Parameter optimisation of Wavelet Denoising for Pulsed Eddy Current Signals Based on particle swarm optimisation algorithm

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NONDESTRUCTIVE TESTING AND EVALUATION 2024年第5期39卷 1210-1224页

作者： Shao, Qianqiu Fan, Songhai Liu, Fenglian State Grid Sichuan Elect Power Res Inst Power Transmiss Transformat & Distribut Technol Ct Chengdu Peoples R China

To ensure the reliable operation of high voltage cables, it is crucial to routinely inspect the lead sealing using pulsed eddy current. But nevertheless, the signals can be impacted by noise in actual engineering. While retaining the useful high-frequency information in the original signal, wavelet denoising can filter the clutter efficiently. However, previous research on wavelet denoising has only used it as a straightforward filtering tool, ignoring the impact of changing the parameters on the actual denoising effect. In this study, a more accurate evaluation index called P-NCC is constructed for pulsed eddy current testing signals with a focus on maintaining the peak information. This index is then adopted as a fitness function for the particle swarm optimisation algorithm to determine the befitting wavelet denoising parameters and denoised signals. The results show that the signal-to-noise ratio of the signals after denoising is approximately 25 dB, and the distortion at the peak position is stable at about 1%. It demonstrates that the comprehensive index P-NCC is a reliable metric for assessing the quality of pulsed eddy current signals. The optimal wavelet denoising parameters for pulsed eddy current signals are found to be the Sym 4 wavelet, 10-layer decomposition and Median threshold function.

关键词： Lead sealing pulsed eddy current wavelet denoising particle swarm optimisation algorithm

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An immune orthogonal learning particle swarm optimisation algorithm for routing recovery of wireless sensor networks with mobile sink

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2014年第3期45卷 337-350页

作者： Hu, Yifan Ding, Yongsheng Hao, Kuangrong Ren, Lihong Han, Hua Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China Donghua Univ Minist Educ Engn Res Ctr Digitized Text & Fash Technol Shanghai 201620 Peoples R China Key Lab Comp Network Shandong Prov Jinan 250014 Peoples R China

The growth of mobile handheld devices promotes sink mobility in an increasing number of wireless sensor networks (WSNs) applications. The movement of the sink may lead to the breakage of existing routes of WSNs, thus the routing recovery problem is a critical challenge. In order to maintain the available route from each source node to the sink, we propose an immune orthogonal learning particle swarm optimisation algorithm (IOLPSOA) to provide fast routing recovery from path failure due to the sink movement, and construct the efficient alternative path to repair the route. Due to its efficient bio-heuristic routing recovery mechanism in the algorithm, the orthogonal learning strategy can guide particles to fly on better directions by constructing a much promising and efficient exemplar, and the immune mechanism can maintain the diversity of the particles. We discuss the implementation of the IOLPSOA-based routing protocol and present the performance evaluation through several simulation experiments. The results demonstrate that the IOLPSOA-based protocol outperforms the other three protocols, which can efficiently repair the routing topology changed by the sink movement, reduce the communication overhead and prolong the lifetime of WSNs with mobile sink.

关键词： wireless sensor networks mobile sink routing recovery orthogonal learning strategy immune algorithm particle swarm optimisation algorithm

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Focusing inverse synthetic aperture radar images with higher-order motion error using the adaptive joint-time-frequency algorithm optimised with the genetic algorithm and the particle swarm optimisation algorithm - comparison and results

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IET SIGNAL PROCESSING 2010年第4期4卷 329-342页

作者： Brinkman, W. Thayaparan, T. Def Res & Dev Canada Dept Natl Def Ottawa ON Canada

algorithms based on the genetic algorithm (GA) and the particle swarm optimisation (PSO) algorithm were designed for focusing inverse synthetic aperture radar (ISAR) images that suffered from degradation because of Doppler smearing. These algorithms optimised the adaptive joint-time-frequency (AJTF) algorithm by replacing the exhaustive search as the primary search tool used to determine focusing parameters. The use of the PSO for ISAR image focusing is a unique application of this evolutionary search. Performance of the GA and the PSO were compared with the PSO producing the optimal results of being able to focus a 2(11) pulse ISAR image with second-order motion error in 9 s or 24% of the cost function calculations required for an exhaustive search. The PSO algorithm was then applied to a 2(11) pulse ISAR image with fourth-order motion error. The PSO algorithm was able to focus this image in 20 s with 33% of the cost function calculations required by the exhaustive search. This study also introduces a new method of determining basis function suitability using the fast Fourier transform.

关键词： fourth-order motion error ISAR image genetic algorithm particle swarm optimisation Integral transforms in numerical analysis adaptive joint-time-frequency algorithm fast Fourier transforms Optical, image and video signal processing higher-order motion error particle swarm optimisation algorithm inverse synthetic aperture radar images synthetic aperture radar radar imaging optimisation techniques Radar equipment, systems and applications genetic algorithms fast Fourier transform

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A NEW HYBRID WHALE particle swarm optimisation algorithm FOR ROBOT TRAJECTORY PLANNING AND TRACKING CONTROL

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MECHATRONIC SYSTEMS AND CONTROL 2024年第1期52卷 48-57页

作者： Zhang, Huakai Yancheng Teachers Univ Logist Support Grp Yancheng 224000 Peoples R China

In recent years, the use of robotic arms in the automation industry has become increasingly prevalent due to advances in science and technology. This paper aims to enhance the control accuracy of manipulators by designing a hybrid optimisation algorithm. The whale algorithm has been optimised and combined with the particle swarm optimisation (PSO) algorithm to achieve this goal. The trajectory of the manipulator is planned through the utilisation of the hybrid algorithm. The membership function of the fuzzy controller is optimised via the hybrid whale PSO algorithm, and combined with the sliding-mode controller, resulting in the design of a fuzzy sliding-mode controller. The results show that in the convergence curve, the iterations for the hybrid algorithm to reach the global optimal solution is 52, 75, and 183. The rate of convergence is faster. In the joint angle change curve, the total time of optimised B-spline interpolation is 19.95 s. The working efficiency of the manipulator is improved. In the calculation of tracking error, the optimised controller has a tracking error between-0.25 and 0.25, resulting in higher trajectory tracking accuracy. Therefore, the hybrid algorithm has good convergence, and the optimised controller has high accuracy, providing good technical support for trajectory planning and tracking control of robotic arms.

关键词： Robotic arm whale algorithm particle swarm optimisation algorithm fuzzy sliding-mode controller trajectory planning tracking control

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Improved strategy of particle swarm optimisation algorithm for reactive power optimisation

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2010年第1期2卷 27-33页

作者： Lu, Jin-gui Zhang, Li Yang, Hong Du, Jie Nanjing Univ Technol Ctr CAD Nanjing 210009 Jiangsu Peoples R China Nanjing Univ Informat Sci & Technol Dept Comp Nanjing Jiangsu Peoples R China

This paper involves the application of particle swarm optimisation in the optimisation problem of reactive power. The optimisation model of reactive power is first introduced and the improved strategy of particle swarm optimisation is proposed for the problem of optimisation of reactive power in this paper. In order to improve the local search ability, the disturbance item is given for the updating equation of the particle in the improved strategy. The numerical examples of standard IEEE-6 and IEEE-30 power systems for the improved strategy of particle swarm optimisation are performed for the reactive power optimisation. The effectiveness of the improved strategy proposed in this paper has been demonstrated preliminarily from the numerical examples.

关键词： reactive power optimisation RPO power flow calculation particle swarm optimisation algorithm PSO

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Parameter calibration method of clustered-particle logic concrete DEM model using BP neural network-particle swarm optimisation algorithm (BP-PSO) inversion method

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ENGINEERING FRACTURE MECHANICS 2023年 292卷

作者： Pan, Xupeng Niu, Yanwei Zhao, Yu Huang, Pingming Wu, Yizhen Chang An Univ Sch Highway Xian 710064 Shaanxi Peoples R China Chang An Univ Coll Future Transportat Xian 710064 Shaanxi Peoples R China

A high precision back propagation neural network-particle swarm optimization (BP-PSO) algorithm inversion method is proposed to calibrate the microparameters of the clustered-particle logic concrete discrete element method model. The calibration targets include initial damage, failure mode, and mechanical properties of the material. The research utilise 243 training datasets generated through orthogonal experimental design and conducted simulations to train the BP neural network. In addition, a parameter sensitivity analysis is employed on the trained BP neural network to quantify the impact level of each microparameter and guide future macroparameter fine-tuning. The results indicate that the mean absolute percentage error of the BP-PSO inversion method is only 3.79%. This research also study on the concrete failure mechanism by using a mesoscale model based on clustered-particle logic, which considered concrete as a three-phase composite composed of mortar matrix, aggregates, and interfacial transition zone. The crack initiation, propagation and coalescence of DEM model show a good agreement with the experimental results.

关键词： Discrete element method Initiation damage BP neural network particle swarm optimisation algorithm Parameter sensitivity analysis Concrete failure mechanism

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