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Analysis of thermal distribution in two end pumping Nd:YAG laser rod using bacterial foraging optimization algorithm

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PRAMANA-JOURNAL OF PHYSICS 2013年第3期80卷 487-505页

作者： Minshed, Mohammed A. Univ Technol Baghdad Laser & Optoelect Engn Dept Baghdad Iraq

There is a strong need for the optimized management of the thermal problem in Nd:YAG laser rod and for a powerful, fast, and accurate modelling tool capable of treating the heat source distribution very close to what it actually is. In this paper, a new optimization algorithm called bacterial foraging optimization algorithm (BFOA) is proposed for simulation of the radial heat distribution. A BFOA discloses a simulation method which delivers the exact temperature distribution in a circularly cylindrical structure with a circularly symmetrical, longitudinally, and transversally non-uniform heat source distribution and circularly symmetrical cooling means. The output power is obtained and compared with previously published experimental measurements for different pump power and a good agreement has been found.

关键词： Solid-state lasers thermal distribution bacterial foraging optimization algorithm

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Modeling of an Air-Core Compulsator and the Pulse Shaping Using optimization algorithm

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IEEE TRANSACTIONS ON PLASMA SCIENCE 2019年第1期47卷 611-618页

作者： Tao, Xuefeng Liu, Kun Wang, Haoze Space Engn Univ Dept Grad Management Beijing 101400 Peoples R China Sun Yat Sen Univ Sch Aeronaut & Astronaut Shenzhen 518101 Peoples R China

Compensated pulsed alternator (compulsator) plays a significant role in the field of pulsed power supply. Different kinds of compulsators have been used to drive high-energy weapons. In this paper, the mathematical model of a two-phase four-pole air-core compulsator is established, taking the current coupling, the change of rotor speed and changing load characteristics into account. Both the self-excitation process and the discharge process are modeled. Simulation results indicate that our model has high accuracy in comparison with the results of the co-simulation method using finite-element method and circuit principle. Moreover, the usage of the mathematical model can improve the simulation efficiency and flexibility. The current pulse requirements of electromagnetic rail gun (EMRG), flash lamp, and electro-thermal-chemical gun (ETCG) are analyzed, respectively, and the pulse shape optimization problem is studied based on the intelligent optimization algorithm. For EMRG and flash lamp, the term "acceleration ratio" is introduced to identify whether a pulse is flat or spiked. For ETCG, the conception of "shape variance" is proposed to evaluate the fitness of its pulse shape. With the help of suitable objection function and intelligent optimization algorithm, the optimal discharge pulse for specific load can be obtained easily.

关键词： Air-core compensated pulsed alternator (compulsator or CPA) mathematical model optimization algorithm pulse shaping techniques

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An advanced Grey Wolf optimization algorithm and its application to planning problem in smart grids

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SOFT COMPUTING 2022年第8期26卷 3789-3808页

作者： Ahmadi, Bahman Younesi, Soheil Ceylan, Oguzhan Ozdemir, Aydogan Istanbul Tech Univ Dept Elect Engn Istanbul Turkey Kadir Has Univ Management & Informat Syst Dept Istanbul Turkey Marmara Univ Dept Elect & Elect Engn Istanbul Turkey

Due to the complex mathematical structures of the models in engineering, heuristic methods which do not require derivative are developed. This paper improves recently developed Grey Wolf optimization algorithm by extending it with three new features: namely presenting a new formulation for evaluating the positions of search agents, applying mirroring distance to the variables violating the limits, and proposing a dynamic decision approach for each agent either in exploration or exploitation phases. The performance of Advanced Grey Wolf optimization (AGWO) method is tested using several optimization test functions and compared to several heuristic algorithms. Moreover, a planning problem in smart grids is solved by considering different objective functions using 33 and 141 bus distribution test systems. From the numerical simulation results, we observe that, AGWO is able to find the best results compared to other methods from 10 and 9 out of 13 test functions for 30 and 60 variables, respectively. Similar to this, it finds best function values for 5 out of 10 fixed number of variable test functions. Also, the result of the CEC-C06 2019 benchmark functions shows that AGWO outperforms 8 for optimization problems from 10. In power distribution system planning problem, better objective function values were determined by using AGWO, resulting a better voltage profile, less losses, and less emission costs compared to solutions obtained by Grey Wolf optimization (GWO) and Particle Swarm optimization (PSO) algorithms.

关键词： optimization algorithm Evolutionary computation Smart grid applications Renewable energy integration

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An optimization algorithm in Ultrawideband Bandpass Wilkinson Power Divider for Controllable Equal-Ripple Level

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IEEE MICROWAVE AND WIRELESS COMPONENTS LETTERS 2020年第9期30卷 861-864页

作者： Bao, Chunxi Wang, Xiaolong Ma, Zhewang Chen, Chun-Ping Lu, Geyu Jilin Univ Coll Elect Sci & Engn State Key Lab Integrated Optoelect Changchun 130012 Peoples R China Saitama Univ Grad Sch Sci & Engn Saitama 3388570 Japan Kanagawa Univ Dept Elect Elect & Informat Engn Yokohama Kanagawa 2218686 Japan

In this letter, an ultrawideband (UWB) bandpass Wilkinson power divider (WPD) is introduced. By using filter synthesis theory and proposed optimization algorithm, all the S-parameters (S-11, S-21 = S-31, S-22 = S-33 and S-32) of the proposed topology could provide an equal-ripple response, and their equal-ripple level and bandwidth can be controlled, respectively. For verification purposes, a prototype UWB WPD has been simulated, fabricated, and measured. The measured and simulated results are matched reasonably well.

关键词： Equal-ripple level optimization algorithm ultrawideband (UWB) Wilkinson power divider (WPD)

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Identification of Multiple Fire Sources in the Utility Tunnel Based on a Constrained Particle Swarm optimization algorithm

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FIRE TECHNOLOGY 2022年第5期58卷 2825-2845页

作者： Liu, Xiaojiang Sun, Bin Xu, Zhao-Dong Liu, Xuanya Xu, Dajun Southeast Univ Joint Lab Smart Disaster Prevent Major Infrastruc China Pakistan Belt & Rd Nanjing 210096 Peoples R China Minist Emergency Management Tianjin Fire Res Inst Tianjin 300381 Peoples R China

Fire disaster is one of the most dangerous disasters in the utility tunnel with plenty of high-voltage and communication cables. Fire source identification is an important part of fire protection in utility tunnel fires. The particle swarm optimization (PSO) algorithm based on limited temperature observations was applied in the multiple fire sources identification problem, and a constrained PSO algorithm is developed for performance improvement. The fire characteristics could be estimated simultaneously, including the fire source location, the maximum temperature value, and the attenuation coefficient. Based on these parameters, the whole temperature distribution of the tunnel could be predicted correspondingly. The feasibility, superiority, and robustness of the proposed algorithm were demonstrated in numerical and experimental scenarios. Results showed that the proposed constrained algorithm could identify the double fire sources with high accuracy, and the identified locations were gathered around the actual ones in comparison with the basic algorithm. The fire source locations and fire states could be estimated under noisy and disturbance situations within an acceptance error. When the measurement noises varied from 0.02 to 0.10, the temperature prediction error of each measurement point changed from [0.1 degrees C, 5.4 degrees C] to [7.3 degrees C, 36.8 degrees C]. Additionally, the closer the distance between fire source and sensors is, and the more sensors allocated, the higher the prediction accuracy is.

关键词： Utility tunnel Fire disaster optimization algorithm Multiple fire sources

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Particle Swarm optimization algorithm With Intelligent Particle Number Control for Optimal Design of Electric Machines

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第2期65卷 1791-1798页

作者： Lee, Jin Hwan Song, Jun-Young Kim, Dae-Woo Kim, Jong-Wook Kim, Yong-Jae Jung, Sang-Yong Sungkyunkwan Univ Dept Elect Elect & Comp Engn Suwon 16419 South Korea Dong A Univ Dept Elect Engn Busan 49315 South Korea Chosun Univ Dept Elect Engn Gwangju 61452 South Korea

In this study, we propose a modified particle swarm optimization (PSO) algorithm, which is an improved version of the conventional PSO algorithm. To improve the performance of the conventional PSO, a novel method is applied to intelligently control the number of particles. The novel method compares the cost value of the global best (gbest) in the current iteration to that of the gbest in the previous iteration. If there is a difference between the two cost values, the proposed algorithm operates in the exploration stage, maintaining the number of particles. However, when the difference in the cost values is smaller than the tolerance values assigned by the user, the proposed algorithm operates in the exploitation stage, reducing the number of particles. In addition, the algorithm eliminates the particle that is nearest to the best particle to ensure its randomness in terms of the Euclidean distance. The proposed algorithm is validated using five numerical test functions, whose number of function calls is reduced to some extent in comparison to conventional PSO. After the algorithm is validated, it is applied to the optimal design of an interior permanent magnet synchronous motor (IPMSM), aiming at minimizing the total harmonic distortion (THD) of the back electromotive force (back EMF). Considering the performance constraint, an optimal design is attained, which reduces back EMF THD and satisfies the back EMF amplitude. Finally, we build and test an experimental model. To validate the performance of the optimal design and optimization algorithm, a no-load test is conducted. Based on the experimental result, the effectiveness of the proposed algorithm on optimal design of an electric machine is validated.

关键词： Electric machine interior permanent magnet synchronous motor (IPMSM) optimal design optimization algorithm particle swarm optimization (PSO)

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Parameter identification of electronic throttle using a hybrid optimization algorithm

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NONLINEAR DYNAMICS 2011年第4期63卷 549-557页

作者： Yuan, Xiaofang Li, Shutao Wang, Yaonan Sun, Wei Wu, Lianghong Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China

Aiming at the problems in parameter identification of an electronic throttle, this paper proposes a novel hybrid optimization algorithm to search the optimal parameter values of the plant. The parameter identification of an electronic throttle is considered as an optimization process with an objective function minimizing the errors between the measurement and identification, and the optimal parameter values of the plant are searched by using a hybrid optimization algorithm. The proposed hybrid optimization algorithm, effective combination of parallel chaos optimization algorithm (PCOA) and simplex search method, preserves both the global optimization capability of PCOA and the accurate search ability of simplex search method. Simulation and experiment results have shown the good performance of the proposed approach.

关键词： Parameter identification optimization algorithm Electronic throttle Chaos optimization algorithm (COA) Simplex search method

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Improving multidimensional normal cloud model to evaluate groundwater quality with grey wolf optimization algorithm and projection pursuit method

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024年 354卷 120279页

作者： Yan, Jiaheng Ren, Ke Wang, Tao Huaiyin Inst Technol Fac Architecture & Civil Engn Huaian 223003 Peoples R China Huaiyin Inst Technol Fac Comp & Software Engn Huaian 223003 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China

Groundwater quality is related to several uncertain factors. Using multidimensional normal cloud model to reduce the randomness and ambiguity of the integrated groundwater quality evaluation is important in environmental research. Previous optimizations of multidimensional normal cloud models have focused on improving the affiliation criteria of the evaluation results, neglecting the weighting scheme of multiple indicators. In this study, a new multidimensional normal cloud model was constructed for the existing onedimensional normal cloud model (ONCM) by combining the projection-pursuit (PP) method and the Grey Wolf optimization (GWO) algorithm. The effectiveness and robustness of the model were analyzed. The results showed that compared with ONCM, the new multidimensional normal cloud model (GWOPPC model) integrated multiple evaluation parameters, simplified the modeling process, and reduced the number of calculations for the affiliation degree. Compared with other metaheuristic optimization algorithms, the GWO algorithms converged within 20 iterations during 20 simulations showing faster convergence speed, and the convergence results of all objective functions satisfy the iteration accuracy of 0.001, which indicates that the algorithm is more stable. Compared to the traditional entropy weights (0.27, 0.23, 0.47, 0.44, 0.29, 0.59, 0.12) or principal component weights (0.38, 0.33, 0.42, 0.34, 0.47, 0.29, 0.38), the weight allocation scheme provided by the GWOPP method (0.50, 0.48, 0.05, 0.38, 0.02. 0.51 and 0.32) considers the density of the distribution of all samples in the data set space. Among all 55 groundwater samples, the GWOPPC model has 21 samples with lower evaluation ratings than the fuzzy evaluation method, and 28 samples lower than the Random Forest method or the WQI method, indicating that the GWOPPC model is more conservative under the conditions of considering fuzziness and randomness. This method can be used to evaluate groundwater quality in other

关键词： Groundwater quality Cloud model optimization algorithm Projection pursuit method

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Parameters determination of the modified J-A model with an optimization algorithm

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INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS 2013年第3期41卷 259-266页

作者： Cong, Haoxi Zou, Liang Zhang, Li Li, Qingmin Shandong Univ Sch Elect Engn Jinan 250061 Shandong Peoples R China North China Elect Power Univ Sch Elect & Elect Engn Beijing Peoples R China

J-A model is widely used in hysteresis modeling as well as performance simulation of the magnetic materials. To achieve preferable adequacy, a modified J-A model is given, however, accurate solution of the parameters is very important for the modified J-A model, especially for over-determined nonlinear equations. In response to solving the over-determined nonlinear equations, this paper first turned the problem of the over-determined nonlinear equations into solving the minimum value of a multivariate function by means of the least square method. While the multivariate function is of high nonlinearity (the function is not continuous and the matrix of the partial derivatives is singular), solution methods using derivative calculation were abandoned, and the direct seeking methods with no derivative calculations (simplex algorithm) were involved to solve the problem. In the end the solution was validated with the use of the genetic algorithm and the simulated annealing algorithm.

关键词： Modified J-A model overdetermined nonlinear equations least square method optimization algorithm simulation

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Defect of Archimedes optimization algorithm and its verification

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SOFT COMPUTING 2023年第2期27卷 701-722页

作者： Ding, Guiyan Wang, Wentao Liu, Hao Tu, Liangping Univ Sci & Technol Liaoning Anshan Peoples R China

Archimedes optimization algorithm (AOA) is a new meta-heuristic algorithm which is based on Archimedes principle and mimics the buoyancy force received by an object in water. The AOA is designed according to physical principles and has been the object of many scholars' research because of its simple and reliable performance. In the course of the study, this paper finds that the AOA is flawed. In the iterative update of the algorithm, the buoyancy principle applied to the object is not completely followed. Through the investigation and analysis of this problem, it is found that the algorithm design which follows the buoyancy principle completely is more advantageously and persuasively, and named the corrected algorithm CAOA. The performance of the CAOA and other comparison optimization algorithms is tested in benchmark functions CEC2017 under equal conditions to verify the ideas proposed in this paper. In the solution accuracy with dimensions of 30 and 50, the comprehensive score of the CAOA is 31 and 33 and ranks first in all algorithms. In the statistical analysis, the CAOA compared with other algorithms one by one, and achieved the best results in all test functions. When compared with other algorithms, the CAOA ranked first. It is hoped that the verification of the ideas in this paper will help the AOA to develop better and optimize the development of the algorithm.

关键词： Meta-heuristic Archimedes optimization algorithm optimization algorithm Defect New verification method

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