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International Conference on Mechatronics and Control (ICMC)

作者： Wang, Hailun Wang, Wanliang Zheng, Jianwei Quzhou Coll Dept Elect Informat Quzhou 324000 Zhejiang Peoples R China Zhejiang Univ Technol Coll Informat Engn Hangzhou 310014 Zhejiang Peoples R China

ISBN: (纸本)9781479925384

differential evolution algorithm with Control Parameter Adaptation and Strategy Adaptation(DE-CPASA) is here introduced to solve the problem of parameter estimation. In DE-CPASA, differential evolution operator is used to search the optimization results of problems, and Gaussian distribution is employed to implement the adaptive control parameters. The strategy adaptation is achieved by the evaluation of fitness function. Simulation test results show that DE-CPASA can obtain more precision solution and have faster convergence. DE-CPASA is employed to estimate the kinetic parameters of Hg oxidation, and an optimization result is obtained.

关键词： differential evolution algorithm adaptive Parameter estimation Hg oxidation

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Combination of the Levenberg-Marquardt and differential evolution algorithms for the fitting of postseismic GPS time series

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ACTA GEOPHYSICA 2021年第2期69卷 405-414页

作者： Li, Jingwei Zhan, Wei Guo, Bofeng Li, Shuiping Guo, Binghui China Earthquake Adm Monitoring & Applicat Ctr 1 Tianjin Peoples R China Tianjin Univ Sch Marine Sci & Technol Tianjin Peoples R China Hefei Univ Technol Sch Civil Engn Hefei Anhui Peoples R China

Postseismic global positioning system (GPS) time series are of fundamental importance for investigating the physical mechanisms of postseismic deformations, as well as the construction and maintenance of terrestrial reference frames. Particularly, methods for constructing accurate fitting models for such time series are critical. Based on the physical features of postseismic deformation models, we propose a new algorithm that combines the strengths of the Levenberg-Marquardt (LM) and differential evolution (DE) algorithms, that is, the LM + DE algorithm. In this algorithm, the parameters are initialised by the constrained DE algorithm;the final parameters of the postseismic model are then solved by the LM algorithm. To validate the proposed method, DE, LM, and LM + DE were compared using synthetic and observational data from the 2011 Tohoku Earthquake. For all tests based on synthetic data, the LM + DE algorithm consistently converged to the global solution and the residual is small, regardless of how the independent parameter was varied. In the 2011 Tohoku earthquake, the parameters calculated by the LM + DE algorithm matched consistently for the global solution with a 100% passing rate after constraints were provided for the ratios of the initial relaxation time parameters. In contrast, the LM and DE algorithms individually achieved passing rates of only 22% and 1%, respectively. These results demonstrate that the proposed LM + DE algorithm effectively solves the initial estimate problem in the fitting of nonlinear postseismic models, and also ensures that the fits are mathematically optimal and consistent with physical reality.

关键词： Postseismic deformation Levenberg– Marquardt differential evolution algorithm GPS time series

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Increasing Objects Localization Precision by Determination of Inertial Sensor Calibration Constants Using differential evolution algorithm

Increasing Objects Localization Precision by Determination o...

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15th International Carpathian Control Conference (ICCC)

作者： Mravec, Tomas Vestenicky, Peter Univ Zilina Dept Control & Informat Syst Fac Elect Engn Zilina Slovakia

ISBN: (纸本)9781479935284

This work solves actual problems of inertial navigation systems - increasing of precision. Main part of the paper deals with determination of accelerometers and gyroscopes calibration constants using a differential evolution algorithm. The determination of constants is based on the difference between trajectory recorded by GPS receiver and trajectory calculated from inertial sensor data. Moreover, the cost function, structure of individuals and used control constants values of differential evolution algorithm are described. The results of work are given by graphical comparison of real trajectory from GPS and trajectories calculated from inertial sensor data with optimized and unoptimized calibration constants.

关键词： inertial navigation accelerometer gyroscope error model differential evolution algorithm

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Research of Grounding Grid Fault Diagnosis Based on differential evolution algorithm

Research of Grounding Grid Fault Diagnosis Based on Differen...

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International Conference on Power System Technology (PowerCon)

作者： Huang, Yuehua Hu, Lihua Cao, Linan Xia, Haifan China Three Gorges Univ Coll Elect Engn & New Energy Yichang Peoples R China

ISBN: (纸本)9781479950324

Grounding grid in the maintenance of power plants and substations safe and reliable operation, guarantee the safety of operators and electrical equipment play an important role. However, due to grounding grid long buried underground, by the electrochemical corrosion in soil environment and the power grid equipment running discharge caused by corrosion, make the conductor that constitute grounding grid severely damaged. The grounding grid that is severely damaged will affects the safety of the normal power supply. Correctly grasping the running status of grounding network, finding hidden danger in time and taking corresponding measures for grounding grid conductor corrosion diagnosis has great significance. Because actual grounding grid only have limited accessible nodes by changing position and every incentive several times measuring, make the accessible nodes can full use, the fault diagnosis equation can be obtained by this method. Due to establishing the fault diagnosis equation is usually an under-determined equation, therefore it is necessary to further study the new diagnosis method to get the optimal solution, thus improve the diagnostic accuracy. This paper adopts a new algorithm that differential evolution algorithm. Finally, in this paper, an instance proved the feasibility of the algorithm.

关键词： Grounding grid differential evolution algorithm fault diagnosis

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Research on Route Obstacle Avoidance Task Planning Based on differential evolution algorithm for AUV 5

Research on Route Obstacle Avoidance Task Planning Based on ...

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5th International Conference on Swarm Intelligence (ICSI)

作者： Li, Jian-Jun Zhang, Ru-Bo Yang, Yu Harbin Engn Univ Coll Comp Sci & Technol Harbin 150001 Peoples R China Dalian Natl Univ Coll Electromech & Informat Engn Dalian 116600 Peoples R China Harbin Univ Commerce Sch Comp & Informat Engn Harbin 150028 Peoples R China

ISBN: (纸本)9783319118970;9783319118963

AUV mission planning route avoidance purpose is to be able to successfully avoid the threat of a number of different levels of obstacles between the start and end of the route, and plan the optimal route planning to meet certain performance indicators. Through the differential evolution algorithm analysis and description, the avoidance route mission planning problem into a multi-dimensional function optimization problems, optimization problems for AUV mission planning route avoidance functions, based on differential evolution algorithm is proposed route obstacle avoidance task planning methods and after a comprehensive analysis and simulation results validate the differential evolution algorithm in high-dimensional function optimization convergence and stability demonstrated good performance.

关键词： differential evolution algorithm Autonomous Underwater Vehicle route avoidance mission planning

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Adaptive memetic differential evolution-back propagation-fuzzy neural network algorithm for robot control

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INFORMATION SCIENCES 2023年第1期637卷

作者： Zheng, Kunming Zhang, Qiuju Peng, Li Zeng, Shuisheng Jiangnan Univ Sch Mech Engn 1800 Lihu Ave Wuxi 214122 Peoples R China Jiangsu Key Lab Adv Food Mfg Equipment & Technol 1800 Lihu Ave Wuxi 214122 Peoples R China Jiangnan Univ Sch Internet Things Engn 1800 Lihu Ave Wuxi 214122 Peoples R China Changzhou Gucoi Intelligent Equipment Technol Res Changzhou 213001 Peoples R China

This study established an adaptive memetic differential evolution-back propagation-fuzzy neural network (AMDE-BP-FNN) control method to achieve high-efficiency and precise control of robots with complex dynamic characteristics while reducing control costs. The adaptive differential evolution (ADE) method was applied to search the optimal parameters in the global scope and delimited the pseudo-global search scope. The memetic differential evolution (MDE) method was used to search for optimal parameters in the pseudo-global scope, and the probability factor was set to decide whether to use the back propagation (BP) algorithm for online optimization. Finally, simulations, experiments, and real-world applications were conducted. The results indicated the high efficiency, high precision, and viability of the proposed AMDE-BP-FNN method.

关键词： Robot Memetic evolution algorithm differential evolution algorithm Back propagation algorithm Fuzzy neural network

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Lagrangian Relaxation Combined with differential evolution algorithm for Unit Commitment Problem 19

Lagrangian Relaxation Combined with Differential Evolution A...

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19th IEEE International Conference on Emerging Technology and Factory Automation (ETFA)

作者： Sum-Im, Thanathip Srinakharinwirot Univ Dept Elect Engn Fac Engn Nakhon Nayok 26120 Thailand

ISBN: (纸本)9781479948451

In this paper, a technique of combining Lagrangian relaxation (LIZ) with a differential evolution algorithm (DEA) method (1.11-DEA) is proposed for solving unit commitment (UC) problem of thermal power plants. The merits of DEA method are parallel search and optimization capabilities. The unit commitment problem is formulated as the minimization of a performance index, which is sum of objectives (fuel cost, start-up cost) and several equality and inequality constraints (power balance, generator limits, spinning reserve, minimum up/down time). The efficiency and effectiveness of the proposed technique is initially demonstrated via the analysis of 10-unit test system. A detailed comparative study among the conventional 1.11, genetic algorithm (GA), evolutionary programming (EP), a hybrid of Lagrangian relaxation and genetic algorithm (IRGA), ant colony search algorithm (ACSA), and the proposed method is presented. From the experimental results, the proposed method has high accuracy of solution achievement, stable convergence characteristics, simple implementation and satisfactory computational time.

关键词： Lagrangian relaxation unit commitment power generation scheduling differential evolution algorithm

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A Study of Task Scheduling Based On differential evolution algorithm in Cloud Computing 6

A Study of Task Scheduling Based On Differential Evolution A...

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6th International Conference on Computational Intelligence and Communication Networks

作者： Xue, Jing Li, Liutao Zhao, SaiSai Jiao, Litao Northwestern Polytech Univ Sch Automat Xian Peoples R China

ISBN: (纸本)9781479969296

In this paper, we put forward a task scheduling algorithm in cloud computing with the goal of the minimum completion time, maximum load balancing degree, and the minimum energy consumption using improved differential evolution algorithm. In order to improve the global search ability in the earlier stage and the local search ability in the later stage, we have adopted the adaptive zooming factor mutation strategy and adaptive crossover factor increasing strategy. At the same time, we have strengthened the selection mechanism to keep the diversity of population in the later stage. In the process of simulation, we have performed the functional verification of the algorithm and compared with the other representative algorithms. The experimental results show that the improved differential evolution algorithm can optimize cloud computing task scheduling problems in task completion time, load balancing, and energy efficient optimization.

关键词： Cloud Computing Task Scheduling differential evolution algorithm

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Training Neural Networks Using Modified differential evolution algorithm For Classification Problems 4

Training Neural Networks Using Modified Differential Evoluti...

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4th International Conference on Computer and Knowledge Engineering (ICCKE)

作者： Ahadzadeh, Behrouz Menhaj, Mohammad Bagher Islamic Azad Univ Fac Comp & Informat Technol Engn Qazvin Branch Qazvin Iran Amirkabir Univ Technol Dept Elect Engn Tehran Iran

ISBN: (纸本)9781479954865

in recent years, progress in the field of artificial neural networks provides a very important tool for complex problems in pattern recognition, data mining and medical diagnosis. The training algorithms of neural networks play an important role for adjustment the network parameters. Different algorithms have been presented for training neural networks;the most common one is the use of gradient descent based algorithms such as back propagation algorithm. Getting trapped in local minima and possessing a very slow converging speed made the gradient based methods problematic. To resolve this many evolutionary algorithms have been adopted for the training of neural networks. In this paper, a modified differential evolution algorithm acronymed as 2sDE is employed as a new training algorithm for feedforward neural networks in order to resolve the problems of local optimization training algorithms such as trapping in local minima and the slow convergence. Effectiveness and efficiency of the proposed method are compared with other training algorithms on various classification problems.

关键词： artificial neural networks training algorithms differential evolution algorithm classification problems

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An Adaptive differential evolution algorithm with Automatic Population Resizing for Global Numerical Optimization

An Adaptive Differential Evolution Algorithm with Automatic ...

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9th International Conference on Bio-Inspired Computing - Theories and Applications (BIC-TA)

作者： Choi, Tae Jong Ahn, Chang Wook Sungkyunkwan Univ SKKU Dept Comp Engn Suwon 440746 South Korea

ISBN: (纸本)9783662450482

An adaptive population resizing algorithm is presented. To improve the performance of DE, an adaptive population size algorithm that makes a balance between exploration-exploitation properties is required. Although adjusting population size is important, many researchers have not focused on this topic. The proposed algorithm calculates the deviation of the dispersed individuals in every certain evaluation counters and executes adjusting the population size based on this information. Therefore, the proposed algorithm can adapt the population size by including or excluding some individuals depending on the progress. The performance evaluation results showed that the proposed algorithm was better than standard DE algorithm.

关键词： differential evolution algorithm Adaptive Parameter Control Population Size Deviation Global Numerical Optimization

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