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检索条件"主题词=Differential evolution algorithm"

共 1334 条记录，以下是31-40 订阅

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differential evolution algorithm-based Kernel Parameter Selection for Fukunaga-Koontz Transform Subspaces Construction 5

Differential Evolution Algorithm-based Kernel Parameter Sele...

引用

Conference on High-Performance Computing in Remote Sensing V

作者： Binol, Hamidullah Bal, Abdullah Cukur, Huseyin Yildiz Tech Univ Dept Elect & Commun Engn TR-34220 Istanbul Turkey

ISBN: (纸本)9781628418569

The performance of the kernel based techniques depends on the selection of kernel parameters. That's why;suitable parameter selection is an important problem for many kernel based techniques. This article presents a novel technique to learn the kernel parameters in kernel Fukunaga-Koontz Transform based (KFKT) classifier. The proposed approach determines the appropriate values of kernel parameters through optimizing an objective function constructed based on discrimination ability of KFKT. For this purpose we have utilized differential evolution algorithm (DEA). The new technique overcomes some disadvantages such as high time consumption existing in the traditional cross-validation method, and it can be utilized in any type of data. The experiments for target detection applications on the hyperspectral images verify the effectiveness of the proposed method.

关键词： differential evolution algorithm Fukunaga-Koontz transform hyperspectral imagery kernel-based methods parameter selection

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differential evolution algorithm Based Load Frequency Control in a Two-Area Conventional and Renewable Energy Based Nonlinear Power System 4

Differential Evolution Algorithm Based Load Frequency Contro...

引用

4th International Conference on the Development in the in Renewable Energy Technology (ICDRET)

作者： Zamee, Muhammad Ahsan Islam, Kazi Khairul Ahmed, Ashik Zafreen, Kazi Rehnuma Bangladesh Univ Dept Elect & Elect Engn 15-1 Iqbal Rd Dhaka 1207 Bangladesh Islamic Univ Technol Board Bazar Dept Elect & Elect Engn Gazipur Bangladesh

ISBN: (纸本)9789843400178

Load Frequency Control (LFC) for any power generating station is a subject of great concern for power system researchers. With the changes of load demand, frequency starts fluctuating which results in deviation in tie line power flow and frequency deviation at consumer end. To overcome this problem, many control techniques have been adopted. In early days fixed value integral/proportional-integral control, Optimal Control, Quantitative feedback theory, pole placement etc. methods were applied. In recent times, neural network, fuzzy logic, genetic algorithm controllers are replacing the conventional techniques. All the control techniques are used to find the optimal values of the PID/PI controller gain parameters (K-p, K-i, K-d) for which system stability is confirmed with minimum of Area Control Error (ACE). differential evolution (DE) which is a newer branch of genetic algorithms has been successfully applied in this problem. In this paper DE based PI controller has been implemented for Hydro-Thermal power plants to find out the optimal value of gain parameters for system stability. Nonlinearity has been considered in governor part of the thermal area for practical scenario. 1% step load changes have been applied to both areas simultaneously and individually to confirm its performance. Desired set of controller gain parameters (K-p, K-i) are selected based on eigenvalue and minimum value of Objective Function. All simulations are done in the MATLAB/SIMULINK environment.

关键词： Load Frequency Control Automatic Generation Control Eigenvalue Objective Function differential evolution algorithm Proportional-Integral Controller

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differential evolution algorithm Using a Dynamic Crossover Parameter with High-Speed Interval Type 2 Fuzzy System 17th

Differential Evolution Algorithm Using a Dynamic Crossover P...

引用

17th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Ochoa, Patricia Castillo, Oscar Soria, Jose Cortes-Antonio, Prometeo Tijuana Inst Technol Div Grad Studies Tijuana Mexico

ISBN: (纸本)9783030044916;9783030044909

The main contribution of this paper is the use of a new concept of type reduction in type-2 fuzzy systems for improving performance in differential evolution algorithm. The proposed method is an analytical approach using an approximation to the Continuous Karnik-Mendel (CEKM) method, and in this way the computational evaluation cost of the Interval Type 2 Fuzzy System is reduced. The performance of the proposed approach was evaluated with seven reference functions using the differential evolution algorithm with a crossover parameter that is dynamically adapted with the proposed methodology.

关键词： differential evolution algorithm Crossover Dynamic parameter adaptation and interval type 2 fuzzy logic

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differential evolution algorithm-BASED RANGE IMAGE REGISTRATION WITH SCALING PARAMETERS 23

DIFFERENTIAL EVOLUTION ALGORITHM-BASED RANGE IMAGE REGISTRAT...

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23rd IEEE International Conference on Image Processing (ICIP)

作者： Li, Taifeng Gao, Liang Pan, Quanke Li, Peigen Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan Peoples R China

ISBN: (纸本)9781467399616

Range image registration is used to align two or more three dimensional (3D) point sets into a common coordinate system. In the matching process, however, the influence of the resolution of 3D scans is ignored generally. In this paper, an enhanced differential evolution (DE) algorithm is proposed to align two different scaling 3D point sets. Specifically, Generalized Procrustes Analysis (GPA) method is employed to accelerate the DE in population initialization. In addition, a novel mutation technique is introduced to improve the accuracy for registration. The proposed method can evaluate all of the transformation parameters synchronously and it is effective for both isotropic and anisotropic scaling registration problem. Experiment results reveal that the proposed algorithm is much superior to other methods in terms of accuracy and robustness for scaling registration problem.

关键词： Range image registration transformation parameters differential evolution algorithm

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differential evolution algorithm Based on Classification and Cross-combination

Differential Evolution Algorithm Based on Classification and...

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International Conference on Fuzzy System and Data Mining (FSDM)

作者： Zhang, Zhong-Ping Jia, Qian-Nan Deng, Chang-Shou Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Hebei Peoples R China Key Lab Comp Virtual Technol & Syst Integrat Hebe Qinhuangdao 066004 Hebei Peoples R China Jiujiang Univ Sch Informat Sci & Technol Jiujiang 332005 Jiangxi Peoples R China

ISBN: (纸本)9781614996194;9781614996187

The differential evolution algorithm is a heuristic random search optimization algorithm based on population differences;however, due to the different natures of different optimization problems, the applicability of the algorithm is limited. This paper is based on the existing algorithms, and uses heuristics thinking to improve the algorithm for blind and random optimization problems, without pertinence of the targets. Additionally, this paper introduces a concept of classification and numerical optimization problems are divided into two categories by analysis of the problem characteristics: basic combination problems and complex transformation problems. Then, a suitable method is chosen for each optimization problem corresponding to the problem category. Simultaneously, cross-combination is integrated into multiple mutation strategies and parameters for the target population to expand the search scope, quickly and accurately converging to the best solution. Then, a differential evolution algorithm based on classification and cross-combination is proposed. Finally, the effectiveness of this method is verified using the test functions set of CEC'05.

关键词： differential evolution algorithm classified optimization cross-combination mutation strategies parameter setting

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differential evolution algorithm Aided Minimum Symbol Error Rate Multi-user Detection for Multi-user OFDM/SDMA Systems

Differential Evolution Algorithm Aided Minimum Symbol Error ...

引用

IEEE 75th Vehicular Technology Conference (VTC Spring)

作者： Zhang, Jiankang Chen, Sheng Mu, Xiaomin Hanzo, Lajos Zhengzhou Univ Sch Informat Engn Zhengzhou Peoples R China Univ Southampton Sch ECS Southampton SO9 5NH Hants England

ISBN: (纸本)9781467309905;9781467309882

A differential evolution (DE) algorithm assisted Minimum Symbol Error Ratio (MSER) Multi-User Detection (MUD) scheme is proposed for multi-user Multiple-Input Multiple-Output (MIMO) aided Orthogonal Frequency-Division Multiplexing / Space Division Multiple Access (OFDM/SDMA) systems. Quadrature Amplitude Modulation (QAM) is employed in most wireless standards by virtue of providing a high throughput. The MSER Cost Function (CF) may be deemed to be the most relevant one for QAM, but finding its minimum is challenging. Hence we propose a sophisticated DE assisted MSER-MUD scheme, which directly minimizes the SER CF of multi-user OFDM/SDMA systems employing QAM. Furthermore, the effects of the DE assisted MSER-MUD's algorithmic parameters, namely those of the population size P-s, of the scaling factor lambda and of the crossover probability C-r on the number of DE generations required for attaining convergence were investigated in our simulations. This allowed us to directly quantify their complexity. The simulation results also demonstrate that the proposed DE assisted MSER-MUD scheme significantly outperforms the conventional MMSE-MUD in term of the system's overall BER and it is capable of narrowing its BER performance discrepancy with respect to the optimal Maximum Likelihood (ML) MUD to about 4dB, while requiring about 200 times less CF evaluations compared to the optimal ML-MUD scheme.

关键词： Orthogonal frequency division multiplexing (OFDM) space division multiple access (SDMA) minimum symbol error rate multi-user detection differential evolution algorithm

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differential evolution algorithm using Stochastic Mutation

Differential Evolution Algorithm using Stochastic Mutation

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IEEE International Conference on Computing, Communication and Automation (ICCCA)

作者： Choudhary, Nikky Sharma, Harish Sharma, Nirmala Rajasthan Tech Univ Kota India

ISBN: (纸本)9781509016662

differential evolution (DE) algorithm is a population-based meta-heuristic for solving various complex optimization problems. Mutation scale factor and Crossover constant are the two important control parameters of the algorithm which are used to direct the search process to the global optima. Fine tuning of both the parameters controls the performance of the algorithm. Literature suggests that due to large step sizes, DE is unable to exploit the promising search space. So, to improve exploitation capability of the algorithm, by taking inspiration from levy flight random walk, a new mutation scale factor, namely stochastic mutation factor is proposed and incorporated with DE. The proposed strategy is named as differential evolution algorithm using Stochastic Mutation (DESMU). Further, to increase the exploration capability of the algorithm, a limit is associated with every solution to count the number of not updating iterations. If this count crosses the pre-defined limit then the solution is randomly initialized. The proposed algorithm is tested over 15 benchmark test functions and compared with basic (DE), and two of its variants namely Scale Factor Local Search differential evolution (SFLSDE) and Self-adaptive differential evolution (SADE). The results reveal that DESMU is a competitive variant of DE.

关键词： differential evolution algorithm Optimization Levy Flight Nature Inspired algorithms

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differential evolution algorithm for Solving Coil Scheduling Problem in Parallel Continuous Annealing Lines 28

Differential Evolution Algorithm for Solving Coil Scheduling...

引用

28th Chinese Control and Decision Conference

作者： Zhao, Shengnan Yang, Yang Northeastern Univ Inst Ind Engn & Logist Optimizat Shenyang 100819 Peoples R China

ISBN: (纸本)9781467397148

This paper studies the coil scheduling problem in parallel continuous annealing lines, which is derived from practical steel production. The problem is to assign the candidate coils to the parallel continuous annealing lines, and make the schedule of the coils in each production line, aiming at reducing the total changeover cost, and improving the production capacity utilization. To solve the problem, a new differential evolution (Sa-PDDE) algorithm is proposed with consideration of the practical production restrictions. Finally, the efficiency of the proposed algorithm is verified by computational experiments.

关键词： Scheduling differential evolution algorithm Continuous annealing

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differential evolution algorithm Based on Adaptive Rank Exponent and Parameters 16th

Differential Evolution Algorithm Based on Adaptive Rank Expo...

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16th CCF Conference on Computer Supported Cooperative Work and Social Computing (ChineseCSCW)

作者： Mai, Weijie Wei, Mingzhu Shen, Fengshan Yuan, Feng Guangzhou & Chinese Acad Sci Inst Software Applicat Technol Guangzhou Peoples R China

ISBN: (纸本)9789811945465;9789811945458

differential evolution algorithm is a very useful and impactful method for handling global numerical optimization issue in the evolutionary algorithm family. However, there are still some shortcomings. Such as, the performance of DE is depend on its mutation strategy and parameters setting. In this article, we present a new fashione differential evolution algorithm which called AFP-DE with adaptive rank exponent and parameters. Compared with the update variant DE algorithms, the experiment shows that performance of AFP-DE is better than them with good performance.

关键词： differential evolution algorithm Adaptive rank exponent and parameters Mutation strategy

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differential evolution algorithm Used for Parameters Reconstruction of Fiber Gratings

Differential Evolution Algorithm Used for Parameters Reconst...

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2nd International Conference on Advances in Materials and Manufacturing Processes (ICAMMP 2011)

作者： Wen, Xiaoyan Qian, Yu Fei, Nie Wuhan Univ Technol Natl Engn Lab Opt Fiber Sensor Wuhan 430070 Peoples R China Wuhan Univ Technol Sch Econ Wuhan Peoples R China

ISBN: (纸本)9783037853283

differential evolution (DE) algorithm has been used to solve the reverse problem of fiber gratings, namely, reconstructing the structure parameters of uniform and chirped fiber gratings from the target reflection spectra. The reconstructed parameters and corresponding spectra are in good agreement with the target ones. Numerical examples demonstrate the highly efficiency and accuracy of DE algorithm in solving the reverse problem of fiber Gratings.

关键词： differential evolution algorithm Reverse problem Fiber gratings Parameter reconstruction

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