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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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Indoor single station location technology for basketball court based on differential evolution algorithm for terminal multi-path fingerprint features

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年第3期25卷 2770-2782页

作者： Huang, Junxian Cao, Zhongwang Jimei Univ Chengyi Coll Xiamen Peoples R China Fujian Chuanzheng Commun Coll Coll Gen Educ 112 Shoushan Rd Fuzhou 350007 Fujian Peoples R China

With the increasing demand for location information, applications based on location information are also showing a diversified trend. Traditional indoor positioning technology often requires multiple base stations or relies on external devices to achieve positioning. To better avoid the problem of synchronization between sites in multi-site positioning systems, this study proposes a positioning method based on differential evolution algorithm in indoor non line of sight scenarios with known access point locations. By collecting multi-path fingerprint features from terminals and utilizing differential evolution algorithm for optimization, a single station positioning system based on differential evolution algorithm was established to achieve indoor single station positioning of basketball courts. The experiment shows that the positioning model can achieve 90% accuracy when the distance between R P is 0.8 m. In terms of positioning performance, a grid spacing of 0.5 meters is the best. Based on differential evolution algorithm, it can not only overcome the issue of error increase caused by multi-path effects but also improve positioning accuracy and stability. This study is essential for the design and optimization of indoor positioning systems.

关键词： differential evolution algorithm single-site localization technology fingerprints convolutional neural network feature collection

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differential evolution algorithm for Ship Path Planning in Open Waters

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JOURNAL OF MARINE SCIENCE AND TECHNOLOGY-TAIWAN 2021年第4期29卷 476-486页

作者： Kang, Yu-Tao Chen, Wei-Jiong Wang, Jin-Hui Shanghai Maritime Univ Coll Ocean Sci & Engn 1550 Hai Gang RdPudong New Area Shanghai Peoples R China

A basic problem in the design of a decision support system for ship collision avoidance is path optimization in a complex and dynamic navigational environment. This paper introduces a new path planning method based on the differential evolution (DE) algorithm to calculate a safe, optimal path for a ship. The algorithm was tested on a set of traffic scenarios typically encountered in open waters. The simulation test results prove the method's ability to solve a path planning problem for ships. We also discuss the optimality, consistency, and performance of the algorithm and provide a comparison of this algorithm with the particle swarm optimization (PSO) algorithm. The comparison results clearly show a significant advantage of the DE algorithm over the PSO algorithm in the areas of output optimality, algorithm consistency, and execution efficiency.

关键词： Collision avoidance Path planning differential evolution algorithm

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differential evolution algorithm Based Self-adaptive Control Strategy for Fed-batch Cultivation of Yeast

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Computer Systems Science & Engineering 2021年第7期38卷 65-77页

作者： Aiyun Hu Sunli Cong Jian Ding Yao Cheng Enock Mpofu Jiangsu Key Lab of IoT Application Technology Wuxi Taihu UniversityWuxi214064China The key Laboratory of Industrial Biotechnology Ministry of EducationSchool of BiotechnologyJiangnan UniversityWuxi214122China Department of Food Processing Technology Harare Institute of TechnologyHarareZimbabwe

In the fed-batch cultivation of Saccharomyces cerevisiae,excessive glucose addition leads to increased ethanol accumulation,which will reduce the efficiency of glucose utilization and inhibit product *** glucose addition limits cell *** properly regulate glucose feed,a different evolution algorithm based on self-adaptive control strategy was proposed,consisting of three modules(PID,system identification and parameter optimization).Performance of the proposed and conventional PID controllers was validated and compared in simulated and experimental *** the simulation,cultivation with the self-adaptive control strategy had a more stable glucose feed rate and concentration,more stable ethanol concentration around the set-point(1.0 g·L^(-1)),and final biomass concentration of 34.5 g-DCW·L^(-1),29.2%higher than that with a conventional PID control *** the experiment,the cultivation with the self-adaptive control strategy also had more stable glucose and ethanol concentrations,as well as a final biomass concentration that was 37.4%higher than that using the conventional strategy.

关键词： Saccharomyces cerevisiae Ethanol accumulation differential evolution algorithm self-adaptive control

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An Adaptive differential evolution algorithm Based on Data Preprocessing Method and a New Mutation Strategy to Solve Dynamic Economic Dispatch Considering Generator Constraints

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COMPUTATIONAL ECONOMICS 2024年 1-34页

作者： Zhao, Ruxin Wang, Wei Zhang, Tingting Liu, Chang Fu, Lixiang Kang, Jiajie Zhang, Hongtan Shi, Yang Jiang, Chao Yangzhou Univ Sch Informat Engn Yangzhou 225127 Jiangsu Peoples R China Yangzhou Polytech Inst Sch Intelligent Mfg Yangzhou 225127 Jiangsu Peoples R China

differential evolution (DE) algorithm is a classical natural-inspired optimiza-tion algorithm which has a good. However, with the deepening of research, some researchers found that the quality of the candidate solution of the population in the differential evolution algorithm is poor and its global search ability is not enough when solving the global optimization problem. Therefore, in order to solve the above problems, we proposed an adaptive differential evolution algorithm based on the data processing method and a new mutation strategy (ADEDPMS). In this paper, the data preprocessing method is implemented by k-means clustering algorithm, which is used to divide the initial population into multiple clusters according to the average value of fitness, and select candidate solutions in each cluster according to different proportions. This method improves the quality of candidate solutions of the population to a certain extent. In addition, in order to solve the problem of insuf-ficient global search ability in differential evolution algorithm, we also proposed a new mutation strategy, which is called "DE/current-to-p1 best&p2 best". This strat-egy guides the search direction of the differential evolution algorithm by selecting individuals with good fitness, so that its search range is in the most promising can-didate solution region, and indirectly increases the population diversity of the algo-rithm. We also proposed an adaptive parameter control method, which can effec-tively balance the relationship between the exploration process and the exploitation process to achieve the best performance. In order to verify the effectiveness of the proposed algorithm, the ADEDPMS is compared with five optimization algorithms of the same type in the past three years, which are AAGSA, DFPSO, HGASSO, HHO and VAGWO. In the simulation experiment, 6 benchmark test functions and 4 engineering example problems are used, and the convergence accuracy, convergence speed and stability are

关键词： differential evolution algorithm Data preprocessing method Adaptive parameter control method Dynamic economic dispatch problem differential evolution algorithm Data preprocessing method DE/current-to-p1 best&p2 best Adaptive parameter control method Dynamic economic dispatch problem

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Bernstein-Levy differential evolution algorithm for numerical function optimization

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NEURAL COMPUTING & APPLICATIONS 2023年第9期35卷 6603-6621页

作者： Civicioglu, Pinar Besdok, Erkan Erciyes Univ Fac Aeronaut & Astronaut Dept Aircraft Elect & Elect Kayseri Turkey Erciyes Univ Fac Engn Dept Geomat Engn Kayseri Turkey

differential evolutionary (DE) algorithm is one of the most frequently used evolutionary computation method for the solution of non-differentiable, complex and discontinuous real value numerical problems. The analytical structure of the mutation and crossover operators used by DE and the initial values of the parameters of the relevant operators affect the problem-solving ability of DE. Unfortunately, there is no analytical method for selecting and initializing the best artificial genetic operators that DE can use to solve a problem. Therefore, there is a need to develop new evolutionary search methods that are parameter-free and insensitive to the artificial genetic operators they use. In this paper, the Bernstein-Levy differential evolution (BDE) algorithm, which has a unique elitist-mutation operator and a Bernstein polynomials-based stochastic parameter-free crossover operator, is introduced. The numerical problem-solving success of BDE is statistically evaluated by using 30 benchmark problems of CEC2014 in the numerical experiments presented. BDE's success in solving the related benchmark problems is statistically compared with six state-of-the-art comparison algorithms. In this paper, three real-world optimization problems are also solved by using the proposed algorithm, BDE. According to statistics generated from the experimental results, BDE is statistically better than comparison methods in solving the related real-world problems.

关键词： differential evolution algorithm Viewshed analysis Hyperspectral imaging Radio coverage maximization

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Research on personalised recommendation method for English online course resources based on hybrid differential evolution algorithm

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2024年第1-2期74卷 73-79页

作者： Zhu, Wei Nanjing Normal Univ Taizhou Coll Sch Foreign Languages & Cultures Taizhou Nanjing Peoples R China

In order to improve personalised satisfaction and recommendation accuracy of English resources, and effectively reduce recommendation time, this article proposes a personalised recommendation method for English online course resources based on hybrid differential evolution algorithm. Firstly, collect English resource data;secondly, matrix decomposition technology is introduced to determine the user's interest information. Then, select the naive Bayesian algorithm to classify the resources. Finally, the fitness value of the individual is calculated, and the personalised recommendation function is designed using the hybrid differential evolution algorithm. The recommendation function is solved through differential mutation, and the final recommendation result is obtained. The results show that the recommendation satisfaction of this method can reach up to 99.5%, the accuracy can reach 99.5% and the recommendation time always does not exceed 4 seconds, effectively improving the personalised precision recommendation effect.

关键词： differential evolution algorithm matrix decomposition Naive Bayes English online courses personalised recommendations

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A novel decomposition-denoising ANFIS model based on singular spectrum analysis and differential evolution algorithm for seasonal AQI forecasting

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第2期44卷 2325-2349页

作者： He, Mingjun Che, Jinxing Jiang, Zheyong Zhao, Weihua Wan, Bingrong Nanchang Inst Technol Sch Sci Nanchang 330099 Jiangxi Peoples R China Nanchang Inst Technol Jiangxi Prov Key Lab Water Informat Cooperat Sens Nanchang Jiangxi Peoples R China Nantong Univ Sch Sci Nantong Jiangsu Peoples R China

Understanding and forecasting air quality index (AQI) plays a vital role in guiding the reduction of air pollution and helping social sustainable development. By combining fuzzy logic with decomposition techniques, ANFIS has become an important means to analyze the data resources, uncertainty and fuzziness. However, fewstudies have paid attention to the noise of decomposed subseries. Therefore, this paper presents a novel decomposition-denoising ANFIS model named SSADD-DE-ANFIS (Singular Spectrum Analysis Decomposition and Denoising-differential evolution-Adaptive Neuro-Fuzzy Inference System). This method uses twice SSA to decompose and denoise the AQI series, respectively, then fed the subseries obtained after the decomposition and denoising into the constructed ANFIS for training and predicting, and the parameters of ANFIS are optimized using DE. To investigate the prediction performance of the proposed model, twelve models are included in the comparisons. The experimental results of four seasons show that: the RMSE of the proposed SSADD-DE-ANFIS model is 1.400628, 0.63844, 0.901987 and 0.634114, respectively, which is 19.38%, 21.27%, 20.43%, 21.27% and 87.36%, 88.12%, 88.97%, 88.71% lower than that of the single SSA decomposition and SSA denoising. Diebold-Mariano test is performed on all the prediction results, and the test results show that the proposed model has the best prediction performance.

关键词： Air quality index forecasting decomposition-denoising Adaptive Neuro-Fuzzy Inference System singular spectrum analysis differential evolution algorithm

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Integrated Renewable Energy Storage System with Enhanced Self-Adaptive differential evolution algorithm on Profit Maximization

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2023年第8期52卷 1474页

作者： Kani, J. Shanmuga Ulagammai, M. Anna Univ Dept Elect & Elect Engn Chennai India Saveetha Engn Coll Dept Elect & Elect Engn Chennai 602105 India

In this work addresses the surge in greenhouse gas emissions and fuel costs resulting from heightened energy demand, especially in developing nations. To counter these challenges, the focus is on optimizing renewable energy sources, which, though advantageous, are weather-dependent and require intricate management. The study introduces the Enhanced Self-Adaptive differential evolution (SADE) algorithm, encompassing solar, battery, and thermal sources, to maximize profitability. Real-time (RT) load profiles are used for performance analysis, comparing the proposed algorithm with existing techniques like PSO, differential evolution (DE) algorithm, and SADE algorithm. Improved energy storage technologies complement the increased utilization of renewable energy, enabling electricity storage during off-peak hours and release during peak demand, providing promising solutions for the energy industry.

关键词： battery energy storage profit maximization Self-Adaptive differential evolution algorithm particle swarm optimization differential evolution algorithm

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A NEW METHOD OF PARAMETER IDENTIFICATION FOR PROTON EXCHANGE MEMBRANE FUEL CELL BASED ON HYBRID PARTICLE SWARM OPTIMIZATION WITH differential evolution algorithm

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THERMAL SCIENCE 2023年第5B期27卷 4209-4222页

作者： Liu, Dong Yang, Xiangguo Guan, Cong Qi, Tianxi Zheng, Qinggen Wuhan Univ Technol Key Lab High Performance Ship Technol Minist Educ Wuhan Peoples R China

With the characteristics of high energy conversion efficiency, high energy density and low operating temperature, the proton exchange membrane fuel cells (PEMFC) have become one of the green energy sources with broad prospects. The establishment of accurate mathematical model of the PEMFC is the basis of simulation and control strategy. At present, some intelligent algorithms have certain drawbacks, and can hardly find the balanced point between precision and computational time. In this study, a novel parameter identification approach combining the hybrid particle swarm optimization (PSO) algorithm with differential evolution, i.e. hybrid DEPSO, is proposed to obtain the unknown parameters in the PEMFC mathematical model and solve the problems of premature convergence of PSO and poor global search ability of differential evolution. Six benchmark functions are applied to verify the performance of the algorithm. The results prove that the hybrid DEPSO can evade local optimum preferably while having swifter convergence rate. Two PEMFC stacks are investigated and modeled. In order to evaluate the accuracy of model, the sum of squared errors between the measured voltage and the estimated output voltage are examined. Numerical results show higher accuracy of the hybrid DEPSO-based model comparing with other recently published optimization approaches. Furthermore, the simulation results indicate that the accuracy of the PEMFC model optimized by the hybrid DEPSO algorithm improves 0.19-1.86%, which can provide a new solution the multi-objective optimization problem and promote the practical application of the PEMFC.

关键词： PEMFC parameters identification PSO algorithm differential evolution algorithm

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