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A distributed d*algorithm with dynamic event-triggered mechanism for resource allocation problems

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS ANd APPLICATIONS 2025年第1期28卷 1-14页

作者： Chen, Baitong Yang, Jianhua Lu, Wei Pedrycz, Witold Sun, Changhai Dalian Univ Technol Sch Control Sci & Engn Dalian 116023 Liaoning Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada Dalian Univ Technol Sch Elect Engn Dalian 116023 Liaoning Peoples R China

distributed d*algorithms have continued to attract attention due to their decentralized nature, strong robustness, and ease of scalability. However, as the scale of networks expands, the implementation of distributed d*algorithms also increases the demand for communication resources. In this study, we introduce a distributed d*algorithm incorporating a dynamic event-triggered communication mechanism to address the widespread issue of resource allocation in numerous engineering disciplines. The proposed d*algorithm significantly reduces communication frequency while maintaining competitive convergence speed. We prove that under the condition of strongly convex and smoothness local cost functions, by selecting appropriate parameters, the d*algorithm can attain linear convergence. The efficacy and benefits of our method are confirmed through several simulation experiments.

关键词： Resource allocation distributed d*algorithm dynamic event-triggered mechanism Linear convergence

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Bayesian model updating method and probabilistic damage identification based on an improved differential evolution adaptive Metropolis d*algorithm

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PROBABILISTIC ENGINEERING MECHANICS 2025年 79卷

作者： Cao, Mingming Peng, Zhenrui Lanzhou Jiaotong Univ Sch Mech Engn Lanzhou 7300730 Peoples R China

The Bayesian finite element model updating (FEMU) method is widely used in structural health monitoring. Traditional Bayesian FEMU methods face challenges such as dimensional limitations, slow convergence, and low computational efficiency. To improve the convergence speed and computational efficiency of the Bayesian FEMU method, this paper proposes a Bayesian FEMU method based on an improved differential Evolution Adaptive Metropolis (dREAM) d*algorithm, named the dREAMZC d*algorithm, and constructs a probabilistic damage identification framework based on this method. The ZC strategies represent the centroid update and sampling difference vectors from past states. The effectiveness of the dREAMZC d*algorithm in FEMU is verified through numerical examples of a simply supported beam and experimental examples of a three-story frame structure. The updated model can serve as a baseline model for probabilistic damage identification. The results show that the proposed dREAMZC d*algorithm has high updating accuracy and fast convergence speed. Using the updated model as the baseline model for probabilistic damage identification can effectively locate the structural damage position and quantify the degree of structural damage, thereby improving the reliability of the damage identification results.

关键词： Centroid update Sampling difference vectors from past states dREAM ZC d*algorithm Bayesian finite element model updating Probabilistic damage identification

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Traceability of surface water pollution based on the SSO+dE d*algorithm

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ALEXANdRIA ENGINEERING JOURNAL 2025年 114卷 112-122页

作者： Jia, dongyan Zhao, Liqiang Song, Jinling Guo, dongliang Liu, Xiaoqing Hebei Normal Univ Sci & Technol Hebei Innovat Ctr Smart Percept & Appl Technol Agr Sch Math & Informat Technol Qinhuangdao 066004 Peoples R China Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Peoples R China Hebei Normal Univ Sci & Technol Sch Math & Informat Technol Qinhuangdao 066004 Peoples R China

River water pollution incidents seriously threaten the ecological environment and people's health. Effective determination of the pollution source information contributes to minimizing the scope of water pollution incidents. A surface water pollution traceability model based on the social spider optimization d*algorithm and differential evolution d*algorithm was proposed to more accurately trace the pollution sources of surface water pollution events. The diffusion law of pollutants in rivers could be described by the migration and diffusion model. Relevant parameters (e.g., water flow velocity and river diffusion coefficient) varied depending on the season and weather. First, parameters in the migration and diffusion model were calibrated using the social spider optimization d*algorithm to coincide with the real hydrological environment. Then, the traceability of pollution source information was completed by simulating mutation, crossing, and selection between individuals based on a differential evolution d*algorithm, which obtained the optimal pollution source location, discharge volume, and discharge time. Finally, the relevant experiment data of the water pollution events between the Jingshi Section and the Puyang Section of the Waterproof River in Ref. [32] were used for simulation experiments. Results were compared with the probabilistic method and coupled density method in Ref. [32]. The average absolute errors of the two methods were 201.0 and 36.43, respectively. The error of the traceability results obtained by the traceability model in the work significantly decreased, with an average absolute error of 1.557. The SSO and dE d*algorithms had good convergence speed and global search capabilities. Besides, the dE d*algorithm is a multi-objective optimization d*algorithm solving multi-dimensional spatial data. Therefore, the surface water pollution traceability model had a high traceability accuracy and certain application value. It reduced the impact of water pollution on

关键词： Water pollution traceability Migration and diffusion model Parameter calibration Social spider optimization d*algorithm differential evolution d*algorithm

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An image encryption d*algorithm based on a novel two-dimensional hyperchaotic map and difference d*algorithm

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NONLINEAR dYNAMICS 2025年第4期113卷 3801-3828页

作者： deng, Yong Tian, Xiaomei Chen, Zhong Xiao, Yongquan Xiao, Yanting Hengyang Normal Univ Coll Comp Sci & Technol Hengyang 421002 Peoples R China Hunan Prov Key Lab Intelligent Informat Proc & Ap Hengyang 421002 Peoples R China

Currently, many existing image encryption d*algorithms have encountered challenges in achieving robustness and efficiency, mainly due to insufficient scrambling or inadequate diffusion mechanisms. These limitations can lead to vulnerabilities, making encrypted images more susceptible to attacks and reducing overall security. To address these issues, this paper proposes a new image encryption d*algorithm (MCLCM-IEA) based on a novel two-dimensional hyperchaotic map (2d-MCLCM). This d*algorithm integrates a difference d*algorithm during diffusion and a double-pointer d*algorithm during scrambling. Additionally, to enhance the security of cipher images, we introduce a plaintext-related weight matrix, which not only expands the key space of MCLCM-IEA but also strengthens its overall security. Benefiting from the superior chaotic properties exhibited by 2d-MCLCM, the strong diffusion efficiency got from the difference diffusion d*algorithm, and the effective scrambling provided by the double-pointer d*algorithm, MCLCM-IEA performs exceptionally well in simulation experiments. The results demonstrate that MCLCM-IEA can effectively resist various illegal attacks and showcase robust security and high efficiency.

关键词： Image encryption Chaos Hyperchaotic map difference d*algorithm Image security

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Scheduling optimization of flexible flow shop with buffer capacity limitation based on an improved discrete particle swarm optimization d*algorithm

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ENGINEERING OPTIMIZATION 2025年第2期57卷 571-597页

作者： Chang, daliang Shi, Haibo Liu, Chang Meng, Fu Chinese Acad Sci Key Lab Networked Control Syst Shenyang Peoples R China Chinese Acad Sci Shenyang Inst Automat Shenyang Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

To address the challenge of production scheduling in flexible flow shops with buffer capacity limitations, a mathematical model is established to minimize the maximum completion time and maximize equipment utilization. To efficiently solve the problem, an improved discrete particle swarm optimization d*algorithm with multi-population reduction (MPREdPSO) is developed. It integrates an elite retention strategy, which significantly contributes to its efficiency and robustness. The d*algorithm stands out for its ability to effectively navigate through potential local optima, a common pitfall in optimization problems, through a Euclid d*algorithm coupled with a sophisticated gene backtracking mechanism. The performance of MPREdPSO is rigorously tested and proven through comparative analyses. Its effectiveness is proven through comparisons with other d*algorithms, namely the discrete particle swarm d*algorithm, genetic d*algorithm, whale d*algorithm, grey wolf optimizer and whale swarm d*algorithm. In addition, MPREdPSO's practical utility is demonstrated in a high-temperature ham sausage production workshop, showcasing its industrial application capabilities.

关键词： Flexible flow shop multi-population reduction discrete particle swarm d*algorithm elite retention

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differential Evolution d*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) d*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 d*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 d*algorithm

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Magnetic Linear Encoder Based on Fuzzy Transition d*algorithm

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IEEE TRANSACTIONS ON INSTRUMENTATION ANd MEASUREMENT 2025年 74卷

作者： Kang, Jian dai, Yong Li, Huan Zhao, Jiarui Zhao, Yitao Hao, Shuanghui Harbin Inst Technol Sch Mechatron Engn Lab Intelligent Equipment Technol Harbin 150001 Peoples R China Shandong Taizhan Electromech Technol Co Ltd Zibo 255130 Peoples R China Shanxi Mech & Elect Design & Res Inst Co Ltd Taiyuan 030009 Peoples R China

In the research into linear displacement sensors, the main research targets are optical grating and magnetic grating-based displacement sensors. However, magnetic grating and optical grating techniques are horizontal measurement methods, which basically require a sliding substrate, a read head, and other components. The resulting magnetic grating or optical grating-based displacement sensors are large in size and cannot realize displacement measurements in small space or vertical measurement scenarios. In this article, a novel and highly accurate linear displacement sensor is designed for use in small space and vertical measurements that uses a completely different structure to conventional magnetic grating or optical grating-based displacement sensors. The proposed magnetic linear displacement sensor consists of two components: a moving shaft with nested magnets and a printed circuit board (PCB) that does not need a moving substrate or a sliding guide. The proposed sensor offers a simple structure, small size, high measurement stability, and high environmental adaptability. The sensor has a completely new structure, and no similar structural design has been reported in the current literature. A permanent magnet ring, an anti-oscillation interference structure, and a system circuit are designed in this article, and the possible oscillating interference, deterministic error, and random interference error characteristics of the sensor are analyzed. A linear displacement sensor decoding d*algorithm based on fuzzy transitions is designed based on the anti-oscillation structure: the corresponding anti-interference d*algorithm is added to it, and the effectiveness of the fuzzy transition decoding d*algorithm is then verified. Finally, an experimental platform is designed for nonlinear error calibration and swing error validation, and the performance specifications of the proposed displacement sensor are verified using this platform. The effect of oscillation error suppression i

关键词： Sensors Magnetic sensors Optical sensors displacement measurement Gratings Optical variables measurement Oscillators Interference Adaptive optics Sensor phenomena and characterization decoding d*algorithm fuzzy transition linear encoder magnetic grating

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StriPre: A VM disk Migration Prefetching d*algorithm

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IEEE ACCESS 2025年 13卷 37514-37521页

作者： Chen, Yuchi Xiong, Tianlei Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China

Virtual machine (VM) cross-cloud migration involves relocating virtual machine instances between different cloud environments, offering advantages such as improved scalability, disaster recovery capabilities, and efficient resource allocation optimization. Two strategies exist for live migration: "pre-copy" and "post-copy". These methods are initially applied to memory migration, but their drawbacks, such as data redundancy and poor virtual machine IO performance are amplified in cross-cloud disk migration due to high network latency, limited transmission bandwidth, and large data volumes, thereby constraining the performance of cross-cloud virtual machine migration. To reduce the performance impact caused by the latency of cross-cloud migration, we present StriPre in this paper. StriPre is a prefetching d*algorithm based on disk access and striped feature design, leveraging the alternating characteristics of disk accesses and performing striping recognition on IO requests. Compared to the commonly used sequential prefetching in memory, StriPre's prefetch hit rate has increased by 1.2x to 2.2x. Our experiments have demonstrated that StriPre can effectively reduce the virtual machine startup time during cross-cloud disk migration. Under extreme cross-cloud latency of 160ms, StriPre can shorten the startup time by 3.48x compared to sequential prefetching.

关键词： Prefetching Virtual machines Costs Cloud computing Prediction d*algorithms Approximation d*algorithms Real-time systems Kernel Business Accuracy Cross-cloud migration data prefetch d*algorithm cloud computing

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Autonomous underwater vehicle path planning using fitness-based differential evolution d*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 d*algorithm is denoted by a fitness-based differential evolution d*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 d*algorithm is conducted on nine case studies of the path planning of AUV, and the comparison is made with other metaheuristic d*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 d*algorithm differential evolution d*algorithm

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A New double-Integration-Enhanced RNN d*algorithm for discrete Time-Variant Equation Systems With Robot Manipulator Applications

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE ANd ENGINEERING 2025年 22卷 8856-8869页

作者： Shi, Yang Chong, Wei Cao, Xinwei Jiang, Chao Zhao, Ruxin Gerontitis, dimitrios K. Yangzhou Univ Sch Informat Engn Yangzhou Peoples R China Yangzhou Univ Jiangsu Prov Engn Res Ctr Knowledge Management & I Yangzhou Peoples R China Jiangnan Univ Sch Business Wuxi 214126 Peoples R China Int Hellen Univ Dept Informat & Elect Engn Thessaloniki 57001 Greece

discrete time-variant equation systems represent a typical and complex problem across various disciplines. With the increasing complexity of systems in various fields, traditional methods have been unable to effectively deal with the current discrete time-variant equation systems, especially in the dynamic engineering problem. Generally speaking, traditional methods are typically limited to considering discrete time-variant equation systems in ideal state, and there is a lack of deep research about more intricate disturbance states. This paper introduces a new recurrent neural network (RNN) d*algorithm, termed the discrete-time double-integration-enhanced RNN (dT-dIE-RNN) d*algorithm, for handling discrete time-variant equation systems (including discrete time-variant linear and nonlinear equation system) under discrete square-time-variant disturbance. Firstly, the continuous-time double-integration-enhanced RNN (CT-dIE-RNN) d*algorithm is presented for solving discrete time-variant linear and nonlinear equation systems by using double-integral-type error function. Secondly, the corresponding discrete-time RNN d*algorithm is presented, and the convergence and precision of such an d*algorithm are theoretically analyzed. Finally, the effectiveness and superiority of the proposed dT-dIE-RNN d*algorithm for solving discrete time-variant linear and nonlinear equation systems are supported by comparative numerical experiments, and these theoretical results are further verified by robot manipulator *** to Practitioners-Generally speaking, previous d*algorithms are unable to guarantee the operation of robot manipulator stably with discrete square-time-variant disturbance. In this study, we design a new RNN d*algorithm which possesses stronger anti-disturbance capability for solving discrete time-variant equation systems, and such the d*algorithm is applied to the tracking control of robot manipulator. In the discrete-time environment, in order to solve the discrete time-variant

关键词： Recurrent neural networks Nonlinear equations Mathematical models Manipulators Automation Vectors Real-time systems Problem-solving Prediction d*algorithms Jacobian matrices double-integration-enhanced RNN d*algorithm discrete time-variant linear and nonlinear equation systems disturbance suppression discrete-time environment residual error

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