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differential evolution algorithm Optimized Dual Mode Load Frequency Controller for Isolated Wind-Diesel Power System with SMES & Fuel Cell

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RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING 2019年第1期12卷 50-60页

作者： Lal, Deepak Kumar Barisal, Ajit K. Tripathy, Manish Veer Surendra Sai Univ Technol Odisha Dept Elect Engn Burla India

Background: This paper presents dynamic performance analysis of isolated wind-diesel power system. A dual mode controller is proposed for pitch control of wind turbine generator. Methods: The parameters of the controller are optimized by differential evolution (DE) algorithm. The hybrid model was simulated with the proposed load frequency controller (LFC) by considering step load perturbation. The minimization of time multiplied integral of absolute error is considered as the objective function. The performance of the proposed controller is compared with the published result of the optimal controller. Further, the performance of the system is investigated by incorporating Super Conducting Magnetic Energy Storage (SMES) and Fuel Cell (FC). Also, the dynamic performance is investigated for changing step load perturbations. Furthermore, the response of the system is analyzed towards random loading. Results: Finally, sensitivity analysis is done by varying the system parameters and operating conditions from their nominal values. Conclusion: The simulation results show that the proposed dual mode DE optimized controller gives better transient and steady state response.

关键词： Load frequency control differential evolution algorithm wind-diesel system pitch control SMES fuel cell

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Ship Power System Optimization based on differential evolution algorithm

International Core Journal of Engineering

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International Core Journal of Engineering 2022年第6期8卷 365-371页

作者： Cong Zhang Xihuai Wang Jianmei Xiao

Ship power system is an independent power system, its stability is greatly affected by the excitation control system of generator. The adjustment of control parameters depends on the experience accumulated by engineers, which has some limitations. The high cost of real ship test and test results. Therefore, this paper uses MATLAB/Simulink to model and simplify the ship power system, and then uses differential evolution algorithm to optimize the parameters. Finally, the typical operating conditions such as sudden load operation and sudden asynchronous motor operation under a single generator are simulated. The performance of the control system is evaluated using ITEA index. The simulation results show that the differential evolution algorithm has better control effect.

关键词： Ship Power System differential evolution algorithm Parameter Optimization

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A Modified differential evolution algorithm Based on Improving A New Mutation Strategy and Self-Adaptation Crossover

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METHODSX 2023年 11卷 102276页

作者： Fadhil, Sadeer Zaher, Hegazy Ragaa, Naglaa Oun, Eman Fac Grad Studies Stat Res Operat Res Giza Egypt Fac Grad Studies Stat Res Math Stat Giza Egypt

The differential evolution algorithm is one of the promising natural inspired population-based metaheuristic algorithms that attracted the attention of researchers in the recent years. This pa-per presents a new mutation strategy called DE/current-to-best/2 that presents a new mutated vector based on utilizing the distance between the best vector and the current vector along with another random vector. In addition, the crossover procedure is self-adapted to cover low locality and high locality based on the iteration number. To obtain the best results of the proposed mod-ified differential evolution algorithm, design of experiments is done to optimize its parameters. The comparative results are done using 11 optimization problems to compare the classical ver-sion of differential evolution algorithm with the new modified version and the results show high efficiency of the proposed DE algorithm in terms of CPU time, evaluation, and accuracy The outline of the work done in this paper can be shown as follows:& BULL;The paper produces a new modification of one of the most promising metaheuristics algo-rithms, the differential evolution algorithm. & BULL;The mutation strategy of the algorithm is modified to work with the current solution, the global best solution, and a random solution. The resulted mutated vector from this procedure is used to produce a new modified crossover solution. & BULL;The crossover procedure is self-adapted to cover low locality and high locality based on the iteration number, where in case of the odd iterations, the high locality is applied to obtain more diversity, and in case of the even iterations the low locality is applied to obtain local neighbor solutions. The comparison is done with the classical version of the algorithm, and the results show efficiency in terms of CPU time, evaluation, and accuracy.

关键词： Optimization Heuristics Metaheuristics differential evolution algorithm Design of experiments

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Histogram modification based pansharpening by using differential evolution algorithm

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第27期34卷

作者： Palancioglu, Haci Mustafa Melikgazi Municipal TR-38030 Kayseri Turkey

Optic geo-observer satellites acquire multispectral images (MSI) with low spatial resolution and panchromatic images (PAN) with high spatial resolution, due to communication band-limits and the necessity of using on-board energy efficiently. Histogram modification is an image processing method that alters the intensity values of an image to improve contrast. The Histogram Modification based Pansharpening (HMP) method has been proposed in this article for injecting high-resolution spatial information of PAN into MSI with limited color degeneration to generate a pansharpened image (PSI). The differential evolution algorithm (DE) has been used to optimize the internal parameters of the HMP. HMP uses three different blind-image quality metrics to avoid from artificial spectral and spatial defects generally appeared in PSI. The quality of PSI generated by HMP has been statistically compared to the PSIs produced by 17 different pansharpening methods by using 10 different image quality metrics. In the tests performed, three different test image sets acquired by geo-observation satellites have been used. Test image sets are covering the areas with different spatial and spectral characteristics. HMP outperforms the other state-of-the-art pansharpening methods used in this article, according to the statistical analysis results. HMP is highly effective at reliably preserving spectral and spatial fidelity.

关键词： differential evolution algorithm histogram modification image fusion Pansharpening

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Optimization of a wave energy converter square array based on the differential evolution algorithm

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OCEAN ENGINEERING 2022年 262卷

作者： He, Zechen Ning, Dezhi Gou, Ying Mayon, Robert Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Peoples R China

The aim of optimizing wave energy converters (WECs) is to maximize the power output whilst reducing the cost of energy production. Here, the optimization of a square array composed of four identical cylindrical, heaving -buoy WECs is considered. Four optimization parameters, i.e., the radius and the draft of the buoy, the damping of the power take-off system, and the spacing of the WECs are considered. Two strategies are carried out to optimize the array in regular waves, and the performance of the optimized array in irregular waves is studied. The dif-ferential evolution algorithm is selected for the multi-parameter optimization. The higher-order boundary element method is used to simulate the interaction between waves and WECs according to linear potential flow theory. The array optimized by the second strategy extracts the maximum power, but its frequency adaptability is inferior to the array optimized by the first strategy. The optimized array in the first strategy can extract relatively large power from the irregular waves, whilst also exhibiting high energy capture efficiency.

关键词： Wave energy converter Array differential evolution algorithm Optimization Linear potential flow theory

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A Photovoltaic Power Predicting Model Using the differential evolution algorithm and Multi-Task Learning

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FRONTIERS IN MATERIALS 2022年 9卷

作者： Pang, Senshen Liu, Jiefeng Zhang, Zhenhao Fan, Xianhao Zhang, Yiyi Zhang, Dongdong Hwang, Goh Hui Guangxi Univ Sch Elect Engn Nanning Peoples R China

The rapid growth of photovoltaic installed capacity exacerbates the power management challenges faced by photovoltaic power stations, emphasizing the importance of accurate and stable photovoltaic generation forecasting. As a result, researchers conducted research and developed several photovoltaic power prediction models. However, many prediction models focus exclusively on the algorithm structure in order to improve model accuracy, oblivious to how the dataset is constructed and divided for the prediction model. This paper proposes a comprehensive model to address this gap. To be more precise, the differential evolution algorithm is constantly looking for optimal values between different populations and determining the best way to construct datasets for prediction tasks. Multi-task learning enables the transfer of knowledge between related tasks via parameter sharing layers, referring to the accuracy and stability of prediction models. Overall, the proposed model achieves high prediction accuracy and stability. The prediction error of the proposed model is less than 450W in RMSE, NRMSE is less than 2.5%, and R-Square is greater than 99% in multiple prediction tasks. Additionally, when compared to other single-task prediction models with an R-Square greater than 96%, the proposed model further reduces the root mean squared error by an average of 28% and the standard deviation of root mean squared error by 54%.

关键词： power management photovoltaic power prediction long short-term memory neural network differential evolution algorithm multi-task learning

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Improved differential evolution algorithm for Multi-Target Response Inversion Detected by a Portable Transient Electromagnetic Sensor

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IEEE ACCESS 2020年 8卷 208107-208119页

作者： Chen, Shudong Zhang, Shuang Luan, Xinqun Liu, Zhaohe Jilin Univ Coll Elect Sci & Engn Changchun 130012 Peoples R China Sci & Technol Near Surface Detect Lab Wuxi 214035 Jiangsu Peoples R China

The superimposed response of multi-target causes difficulty in locating and characterizing each target when detecting unexploded ordnance with a portable transient electromagnetic sensor, constructed with a single-layer transmitting coil and five three-component receiving coils. differential evolution (DE) algorithm is improved here with Gram-Schmidt orthogonalization and position rearrangement for superimposed response inversion based on the multisource model, which represents the multi-target response with a set of magnetic dipoles distributed over the interrogated area. The Gram-Schmidt orthogonalization turns the coefficient matrix of each target into an orthonormal basis. Accordingly, the best magnetic polarization tensor can be directly extracted from the superimposed response without inverting large and potentially ill-conditioned matrices. The position rearrangement groups the positions of individuals in the contemporary population to maximize the likelihood that the positions in the same group belong to the same target. The convergence speed of multi-target inversion is accelerated with the crossover operation of DE algorithm performed within the groups. Simulated experiment results show that the error in estimated position and characteristic response for improved DE algorithm is only 10% of that of the conventional DE algorithm. Field experiment is also conducted, and its results show that the error in estimated position for improved DE algorithm is only 20% of that of the conventional DE algorithm. The improved DE algorithm can accurately estimate the position and characteristic response of each target from superimposed response.

关键词： Statistics Sociology Mathematical model Electromagnetics Transient analysis Electromagnetic interference Silicon Unexploded ordnance portable transient electromagnetic sensor differential evolution algorithm Gram– Schmidt orthogonalization multisource model

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Model Research of Vehicle Intelligent Scheduling Problem in Distribution Center Based on Improved differential evolution algorithm

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JOURNAL OF INTERCONNECTION NETWORKS 2022年第SUPP02期22卷 2143033-2143033页

作者： Cui, Wei Xu, XiaoLing Lanzhou JiaoTong Univ Sch Traff & Transportat Lanzhou Gansu Peoples R China LanZhou City Univ Informat Network Ctr Lanzhou Gansu Peoples R China

With the continuous expansion of the city scale and the increasing population, people have put forward higher requirements for the speed, reliability and economy of logistics distribution. Although there have been many achievements in the research on the vehicle optimal scheduling decision model and its solution algorithm, there are some problems such as low solution efficiency, long time consumption, and easy to fall into the local optimal solution. The purpose of this paper is to study the model of vehicle scheduling problem in distribution center based on improved differential evolution algorithm. This paper firstly makes a simple analysis of the vehicle distribution route optimization problem, and points out the main factors that constitute the route optimization problem;then analyzes the principle of the differential evolution algorithm, and on this basis, the differential evolution algorithm is improved. In the experimental part, this paper takes a distribution center as the research object, and proposes the implementation steps of the algorithm of the vehicle scheduling problem based on the improved differential evolution algorithm. And test the running cost of the improved differential evolution algorithm and differential evolution algorithm. Experimental results show that applying the improved differential evolution algorithm to vehicle scheduling problems has certain practicality. When the number of iterations reaches 60, the running cost of the improved differential evolution algorithm is 1,200 yuan, and the running cost of the differential evolution algorithm is 1,300 yuan.

关键词： differential evolution algorithm distribution center vehicle intelligent scheduling route optimization problem model

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Weighted differential evolution algorithm for numerical function optimization: a comparative study with cuckoo search, artificial bee colony, adaptive differential evolution, and backtracking search optimization algorithms

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NEURAL COMPUTING & APPLICATIONS 2020年第8期32卷 3923-3937页

作者： Civicioglu, Pinar Besdok, Erkan Gunen, Mehmet Akif Atasever, Umit Haluk Erciyes Univ Fac Aeronaut & Astronaut Dept Aircraft Elect & Elect Kayseri Turkey Erciyes Univ Engn Fac Dept Geomat Engn Kayseri Turkey

In this paper, weighted differential evolution algorithm (WDE) has been proposed for solving real-valued numerical optimization problems. When all parameters of WDE are determined randomly, in practice, WDE has no control parameter but the pattern size. WDE can solve unimodal, multimodal, separable, scalable, and hybrid problems. WDE has a very fast and quite simple structure, in addition, it can be parallelized due to its non-recursive nature. WDE has a strong exploration and exploitation capability. In this paper, WDE's success in solving CEC' 2013 problems was compared to 4 different EAs (i.e., CS, ABC, JADE, and BSA) statistically. One 3D geometric optimization problem (i.e., GPS network adjustment problem) and 4 constrained engineering design problems were used to examine the WDE's ability to solve real-world problems. Results obtained from the performed tests showed that, in general, problem-solving success of WDE is statistically better than the comparison algorithms that have been used in this paper.

关键词： Cuckoo search algorithm Artificial bee colony algorithm differential evolution algorithm Backtracking search optimization Particle swarm optimization

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Kinship-based differential evolution algorithm for unconstrained numerical optimization

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NONLINEAR DYNAMICS 2020年第2期99卷 1341-1361页

作者： Formica, Giovanni Milicchio, Franco Roma Tre Univ Dipartimento Architettura Via Madonna Monti 40 Rome Italy Roma Tre Univ Dipartimento Ingn Via Vasca Navale 79 Rome Italy

We propose a modification of the standard differential evolution (DE) algorithm in order to significantly make easier and more efficient standard DE implementations. Taking advantages from chaotic map approaches, recently proposed and successfully implemented for swarm intelligence-based algorithms, our DE improvement facilitates the search for the best population and then the optimal solution. More specifically, we work with a genetic memory that stores parents and grandparents of each individual (its kin) of the population generated by the DE algorithm. In this way, the new population is carried out not only on the basis of the best fitness of a certain individual, but also according to a good score of its kin. Additionally, we carried out a wide numerical campaign in order to assess the performances of our approach and validated the results with standard statistical techniques.

关键词： Optimization differential evolution algorithm Chaotic maps

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