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differential evolution algorithm based Double Integral Sliding Mode Control for Maximum Power Point Tracking of a standalone photovoltaic system

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RENEWABLE ENERGY 2025年 244卷

作者： Rizki, H. Boufounas, E. -m. El Amrani, A. El Amraoui, M. Bejjit, L. Moulay Ismail Univ Meknes Higher Sch Technol Meknes LASMAR Lab MIN Res Grp Meknes Morocco Moulay Ismail Univ Meknes Fac Sci & Technol REIPT Lab BP 509 Boutalamine Errachidia Morocco Moulay Ismail Univ Meknes Fac Sci Meknes LASMAR Lab Meknes Morocco

The purpose of this study is to develop a robust control strategy based on the Sliding Mode Control (SMC) method and its advanced form of Double Integral Sliding Mode Control (DISMC) enhanced by a differential evolution (DE) algorithm. This approach focuses on offering a strong and effective method to adjust the controller parameters to minimize the tracking error between the PV output voltage and the reference. The DE algorithm uses initialization, mutation, crossover, and selection phases to accurately find the optimal controller coefficients for effectively implementing the control strategy of DISMC. The present paper highlights the superior performance of DE-DISMC compared to other controllers optimized with conventional methods, demonstrating its ability to provide the highest stability, accuracy, and efficiency to optimize the Maximum Power Point Tracking (MPPT) process. Using real climatic data from Errachidia city which is located in the south-east of Morocco, this work illustrates DE-DISMC's ability to maintain the PV system's peak performance in the face of varying environmental conditions, and the Lyapunov analysis confirms the system's stability. The results of this study are evaluated using various statistical and analytical metrics, including response time, magnitude of chattering, steady-state error, and efficiency. The findings highlight the high performance of the proposed DE-DISMC controller, especially under real climatic test conditions, achieving a response time of 1.1 ms, which is 58% faster than DE-ISMC and 89% than DE-CSMC, along with a negligible chattering magnitude of 2.8 x 10-6 V. It also demonstrates strong resilience and effectiveness, reaching a minimal steady-state error of 0.00014 V and a high efficiency of 99.99%. This balance makes DE-DISMC a reliable control solution, especially in changing environmental conditions.

关键词： Photovoltaic systems Maximum Power Point Tracking Sliding Mode Control Double Integral Sliding Mode Control Intelligent control differential evolution algorithm

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Real-time torque prediction for ultrasonic motors using an attention-based BiLSTM model and improved differential evolution algorithm

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MEASUREMENT 2025年 251卷

作者： Wang, Yanbo Sasamura, Tatsuki Mustafa, Abdullah Morita, Takeshi Univ Tokyo Dept Precis Engn Hongo 7-3-1Bunkyo Ku Tokyo 1138656 Japan Univ Tokyo Dept Human & Engn Environm Studies Kashiwa No Ha 5-1-5 Kashiwa 2778563 Japan

Ultrasonic motors (USMs), characterized by their miniaturization, high precision, and low noise, are widely utilized in robotics, medical devices, and aerospace applications. However, existing torque control methods are heavily dependent on sensors, which not only increase system cost and complexity but also restrict the deployment of USMs in space-constrained environments, thereby undermining their miniaturization advantages. Furthermore, the complex nonlinear torque characteristics and significant temperature effects of USMs have made traditional torque prediction methods based on physical models inadequate to meet the high-precision requirements of practical applications. To address these challenges, a real-time torque prediction method based on a hybrid attention mechanism, Hodrick-Prescott (HP) decomposition, and bidirectional long short-term memory (BiLSTM) network is proposed in this study. HP decomposition is employed to effectively capture both long-term trends and short-term fluctuations in time series data. The hybrid attention mechanism further highlights key input variables by distributing weights across time steps and feature dimensions. Finally, an improved differential evolution algorithm is applied to optimize the attention weights, enhancing model performance and reducing manual tuning effort. The proposed method's superiority is confirmed by experimental results, which demonstrate high prediction accuracy and rapid response under various operating conditions. These qualities make the method highly suitable for real-time, high-precision, and miniaturized applications such as small robotic joints driven by USMs and precise medical machines.

关键词： Ultrasonic motors Torque control Long short-term memory network Attention mechanism differential evolution algorithm

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A Q-learning improved differential evolution algorithm for human-centric dynamic distributed flexible job shop scheduling problem

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JOURNAL OF MANUFACTURING SYSTEMS 2025年 80卷 794-823页

作者： Li, Xixing Guo, Ao Yin, Xiyan Tang, Hongtao Wu, Rui Zhao, Qingqing Li, Yibing Wang, Xivincent Hubei Univ Technol Sch Mech Engn Hubei Key Lab Modern Mfg & Qual Engn Wuhan Peoples R China Wuhan Univ Technol Sch Mech & Elect Engn Wuhan Peoples R China KTH Royal Inst Technol Dept Prod Engn Stockholm Sweden

Traditional scheduling less account of human-related dynamic events: worker skill degradation and worker mandatory rest. However, in actual production, workers experience fatigue accumulation that decreases work efficiency, thereby decreasing the precision of jobs, increasing rework rates, and even elevating processing risks. It conflicts with the idea of industrial resilience and human well-being for Industry 5.0. Therefore, a humancentric dynamic distributed flexible job shop scheduling problem (HDDFJSP) has been researched in this paper. Firstly, a multi-objective mathematical model of HDDFJSP is proposed to minimize makespan, worker fatigue, and scheduling deviation. Secondly, a Q-learning improved differential evolution (QLIDE) is designed to solve the HDDFJSP. In the QLIDE, a new four-layer encoding method and two initialization strategies are proposed to generate a high-quality initial population and a novel mutation strategy and two auxiliary mutation methods are designed to enhance the algorithm's exploitation capabilities. Furthermore, three neighborhood search strategies are introduced and combined with mutation operations as part of the Q-learning action phase to improve population convergence and diversity. Thirdly comparative test with four other well-known algorithms has been conducted and the results demonstrate the significant superiority of the QLIDE. Finally, the QLIDE is applied to solve a real case of a labor intensive hydraulic cylinder manufacturing enterprise. The results indicate that considering rescheduling can effectively help production managers to handle dynamic event of humans during the intelligent manufacturing systems.

关键词： Human-Centric differential evolution algorithm Q -learning Dynamic rescheduling

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Autonomous underwater vehicle path planning using fitness-based differential evolution algorithm

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JOURNAL OF COMPUTATIONAL SCIENCE 2025年 85卷

作者： Gupta, Shubham Kumar, Ayush Kumar, Vinay Singh, Shitu Sachin Gautam, Mayank Motilal Nehru Natl Inst Technol Allahabad Dept Math Prayagraj 211004 India Motilal Nehru Natl Inst Technol Allahabad Dept Elect & Commun Engn Prayagraj 211004 India United Univ Dept Appl Sci Prayagraj 211012 Uttar Pradesh India

The enhanced capabilities of autonomous underwater vehicles (AUVs) will facilitate sustainable exploration and utilization of maritime resources through improved precision in underwater mapping, resource extraction, and environmental surveillance. Enhanced navigation and communication systems will bolster the robustness and flexibility of AUVs, opening up new avenues for research and operations in demanding underwater conditions. The objective of this initiative is to optimize the performance of AUVs by developing sophisticated navigation methodologies specifically designed for complex marine environments. To achieve this goal, this paper proposes a modified structure of the well-known metaheuristic called differential evolution (DE). The proposed algorithm is denoted by a fitness-based differential evolution algorithm (FDE). Through the utilization of path planning techniques and the application of the proposed FDE to enhance navigation, this paper seeks to overcome obstacles such as underwater barriers, restricted communication, and limited visibility. These enhancements are anticipated to notably elevate the efficacy and cognitive capabilities of AUVs. The validation of the proposed FDE algorithm is conducted on nine case studies of the path planning of AUV, and the comparison is made with other metaheuristic algorithms. The comparison indicates the effectiveness of the FDE in solving the AUV path planning problem.

关键词： Autonomous underwater vehicles Underwater environment modeling Threat modeling Path finding algorithm differential evolution algorithm

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A multi-strategy self-adaptive differential evolution algorithm for assembly hybrid flowshop lot-streaming scheduling with component sharing

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SWARM AND evolutionARY COMPUTATION 2025年 92卷

作者： Lu, Yiling Tang, Qiuhua Yu, Shujun Cheng, Lixin Wuhan Univ Sci & Technol Minist Educ Key Lab Met Equipment & Control Technol Wuhan Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan Peoples R China

Lot-streaming helps to achieve a more balanced utilization of parallel machines and more timely assembly of components, while component sharing increases the flexibility and commonality of assembly operations. Thus, this work addresses an assembly hybrid flowshop lot-streaming scheduling problem with component sharing. A mixed-integer linear programming model is formulated to scrutinize the coupling relations among variables i.e. sub-lot splitting, machine allocation, processing sequencing, and assembly sequencing, and to minimize the maximum completion time and work-in-process inventory lexicographically. To solve the above problem efficiently, a multi-strategy self-adaptive differential evolution (MSDE) algorithm is developed. In MSDE, three problem-specific strategies that consider component integrity and specific requirements of production and assembly are integrated to enhance the initial population in terms of diversity and solution quality. A Q-learningbased selection mechanism is proposed to self-adaptively select an appropriate combination from mutation and crossover operators for achieving a balance between exploration and exploitation. An inventory reduction strategy is appended to largely reduce work-in-process components without extending completion time. Four conclusions are drawn from extensive experiments: (1) The ensemble of three population initialization strategies is superior to each individual one;(2) The Q-learning-based optimizer selection is more effective and robust than the single optimizer-based one;(3) The work-in-process inventory reduction strategy demonstrates remarkable effectiveness for most solutions;(4) MSDE outperforms the existing state-of-the-art algorithms in most cases.

关键词： Assembly flowshops Lot-streaming Component sharing differential evolution algorithm

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A differential evolution algorithm with diversity dynamic adjustment and two-phase constraint handling strategy for solving a pension fund investment problem under market uncertainty

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APPLIED SOFT COMPUTING 2025年 172卷

作者： Wu, Xiang Meng, Haozheng Yuan, Xiaolan Zheng, Qunxian Lin, Jinxing Zhang, Kanjian Guizhou Normal Univ Sch Math Sci Guiyang 550001 Peoples R China Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Anhui Polytech Univ Sch Elect Engn Wuhu 241000 Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Southeast Univ Sch Automat Nanjing 210096 Peoples R China Southeast Univ Key Lab Measurement & Control CSE Minist Educ Nanjing 210096 Peoples R China

This paper considers the optimization of investment strategies (ISs) for pension fund under market uncertainty. The target for this problem is to maximize the overall desired wealth (ODW) at the closed of the planning period while considering the risk of market uncertainty. To begin with, this problem is modeled as a discrete- time stochastic dynamic optimization problem (DTSDOP) with hard constraints (HCs) and uncertain constraints (UCs). Different from existing models, the main goal is to maximize ODW, rather than maximizing the wealth for individual investors, and to make sure that the pension fund is able to fulfill all obligations with a desired probability, this model introduces UCs instead of constraints on the variance. Additionally, this model takes into account the risk of market uncertainty, which helps to compare the optimal ISs with and without market uncertainty. Then, a deterministic transformation method is proposed for converting DTSDOP to a deterministic problem with HCs by fully utilizing the information of DTSDOP. Following that, a diversity dynamic adjustment and two-phase constraint handling strategy-based differential evolution algorithm (DDA-TPCHS-DEA) is proposed for attaining a global optimum for the discrete-time deterministic dynamic optimization problem (DTDDOP), in which constraints and the cost function are balanced by a diversity dynamic adjustment strategy, and a two-phase constraint handling strategy is utilized for finding the boundary knowledge for feasible and infeasible domains. Finally, the superiority of the proposed method is illustrated utilizing 21 test functions, an optimal design problem for robot grippers, and a pension fund investment problem under market uncertainty. The source code of the proposed method and its supplementary material are available in the following GitHub repository: https://***/xwu-gznu/DDA-TPRHS-DEA

关键词： Pension fund Investment strategies Stochastic dynamic optimization Global optimum differential evolution algorithm

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Two-Stage Deep Feature Selection Method Using Voting differential evolution algorithm for Pneumonia Detection From Chest X-Ray Images

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第1期9卷 918-932页

作者： Ouyang, Haibin Liu, Dongmei Li, Steven Ding, Weiping Zhan, Zhi-Hui Guangzhou Univ Sch Mech & Elect Engn Guangzhou 510006 Peoples R China RMIT Univ Sch Business & Law Melbourne Vic 3000 Australia Nantong Univ Sch Artificial Intelligence & Comp Sci Nantong 226019 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

Chest X-ray images play a crucial role in pneumonia diagnosis, with deep transfer learning being a widely adopted method for pneumonia detection. However, effectively handling feature data extracted from deep models without succumbing to the challenges of feature dimensionality remains a formidable task. In response to this complex issue, we propose a novel two-stage deep feature selection (FS) method utilizing the voting differential evolution (VDE) algorithm. In this approach, a dimension adaptive search strategy is meticulously devised to ensure robust feature selection while concurrently reducing the dimension. To expedite the optimization process, we devise a CR adaptive adjustment method to enhance the efficiency of the algorithm. Notably, an important aspect of our approach is the introduction of a novel DE algorithm that integrates a voting mechanism. This synergistic fusion allows a comprehensive analysis of crucial feature relationships to mitigate the risk of algorithmic entrapment in local optima. Additionally, we propose a dynamic feature evaluation function to avert the oversight of feature sets with optimal classification accuracy during later stages of the algorithm, thereby preserving discriminative features. The method is verified on an open Chest X-Ray Images dataset, achieving 99.04% average precision, 98.67% average accuracy, 99.13% average recall, and 19.93% average feature dimension reduction ratio. The experimental findings reveal that the presented method outperform prevailing state-of-the-art algorithms.

关键词： Feature extraction Pneumonia X-ray imaging Heuristic algorithms Convolutional neural networks Accuracy Training differential evolution algorithm convolutional neural network optimization speed feature selection

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Optimizing mixed-model assembly line efficiency under uncertain demand: A Q-Learning-Inspired differential evolution algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 200卷

作者： Meng, Kai Li, Shujuan Han, Zhoupeng Xian Univ Technol Sch Mech & Precis Instrument Engn Xian 710048 Shaanxi Peoples R China

Modern manufacturing heavily relies on mixed-model assembly lines to streamline production processes for various product configurations. However, most existing research in this area primarily focuses on deterministic demand scenarios, leaving the challenges posed by uncertain demand relatively unexplored. Such uncertainty can significantly impact assembly line efficiency, resource utilization, and throughput rates. This paper explores the complexities of balancing and sequencing in mixed-model assembly lines, particularly under conditions of uncertain demand. The proposed approach includes a robust mixed-integer linear programming model formulated to optimize production efficiency across diverse scenarios characterized by uncertain demand. To address this complex problem, a novel Q-Learning-Inspired differential evolution algorithm (QL-DE) has been developed. This algorithm utilizes a population-based evolutionary operator, an intra-population crossover operator, six task-centric and three product-centric neighborhood exploration operators, along with a Q-learning-inspired strategy. These components collectively enable the QL-DE algorithm to adaptively handle uncertain demand while optimizing assembly line processes. Finally, through a comparative analysis with five variants and five evolutionary algorithms, the QL-DE approach demonstrates its superior capability in efficiently addressing uncertain demand scenarios and optimizing the performance of mixed-model assembly lines.

关键词： Mixed-model assembly lines Uncertain demand Balancing and sequencing differential evolution algorithm Q -learning inspired strategy

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Design optimization of sandwich composite cylinder using modified differential evolution algorithm

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MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES 2025年

作者： Bhadra, Manash Kumar Vinod, G. Jain, Atul Vikram Sarabhai Space Ctr Space Transportat Syst Thiruvananthapuram India Indian Inst Technol Kharagpur Mech Engn Dept Kharagpur India

This study presents a modified differential evolution algorithm for optimizing sandwich composite cylinders under hydrostatic pressure. The algorithm enforces balanced laminates using a while loop to enforce balanced laminates in both the initial population and trial vectors, reducing computational demands and accelerating convergence. Unlike previous studies, it demonstrates another novelty by including the number of plies and core thickness as design variables, alongside ply angles and layup sequences, enabling practical, customizable optimization. The approach increases failure load by 35.4% while reducing mass and demonstrates robustness by tolerating stochastic layup errors, resulting in resilient designs with enhanced performance.

关键词： Sandwich structures differential evolution algorithm finite element analysis hydrostatic pressure composite

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Improvement of the Approximate Method for Determining the Average Vertical Stress Increase Below the Rectangular Foundation Using differential evolution algorithm

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年 1-20页

作者： Dagdeviren, Ugur Kutahya Dumlupinar Univ Fac Engn Dept Civil Engn Kutahya Turkiye

External loads transferred from the structure's foundations to the soil induce stress increases in the soil stratum. Since stress increases within the soil mass vary with depth and across the plane at a given depth, approaches that estimate the average stress increase under foundations can be advantageous for effective foundation design. This study aims to develop optimization-based approximate methods for calculating average vertical stress increases with higher accuracy than the conventional 2V:1H method for rectangular foundations with different L/B ratios. For this purpose, vertical stress increases within the foundation projection at 120 different depths for 12 different L/B ratios were numerically calculated using Boussinesq's stress expressions. The model parameters of the proposed approximate models, such as expansion slopes (k or k1, k2) and normalized critical depth (zcr/B), for each L/B ratio were optimized using the differential evolution algorithm. The proposed three-parameter approximate method achieved the highest accuracy, reducing the RMSE values by an average of 53% compared to the conventional method, while the one-parameter model reduced the RMSE by 9%. The maximum absolute errors in the three-parameter model remained between 0.0217 and 0.0283, with R2 values greater than 0.9972. Building upon and improving the conventional method, this study presents a practical and novel three-parameter method that provides a more reliable and accurate estimation of the average vertical stress increase under flexible rectangular foundations, significantly reducing errors. This study contributes to geotechnical engineering by improving the accuracy of stress increase prediction models, potentially leading to more economical and safer foundation designs.

关键词： Vertical stress increase Influence factor Rectangular foundation Approximate method differential evolution algorithm

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