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  • Open Access


    Adaptive Multi-Updating Strategy Based Particle Swarm Optimization

    Dongping Tian1,*, Bingchun Li1, Jing Liu1, Chen Liu1, Ling Yuan1, Zhongzhi Shi2

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2783-2807, 2023, DOI:10.32604/iasc.2023.039531

    Abstract Particle swarm optimization (PSO) is a stochastic computation technique that has become an increasingly important branch of swarm intelligence optimization. However, like other evolutionary algorithms, PSO also suffers from premature convergence and entrapment into local optima in dealing with complex multimodal problems. Thus this paper puts forward an adaptive multi-updating strategy based particle swarm optimization (abbreviated as AMS-PSO). To start with, the chaotic sequence is employed to generate high-quality initial particles to accelerate the convergence rate of the AMS-PSO. Subsequently, according to the current iteration, different update schemes are used to regulate the particle search process at different evolution stages.… More >

  • Open Access


    A Hybrid Heuristic Service Caching and Task Offloading Method for Mobile Edge Computing

    Yongxuan Sang, Jiangpo Wei*, Zhifeng Zhang, Bo Wang

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2483-2502, 2023, DOI:10.32604/cmc.2023.040485

    Abstract Computing-intensive and latency-sensitive user requests pose significant challenges to traditional cloud computing. In response to these challenges, mobile edge computing (MEC) has emerged as a new paradigm that extends the computational, caching, and communication capabilities of cloud computing. By caching certain services on edge nodes, computational support can be provided for requests that are offloaded to the edges. However, previous studies on task offloading have generally not considered the impact of caching mechanisms and the cache space occupied by services. This oversight can lead to problems, such as high delays in task executions and invalidation of offloading decisions. To optimize… More >

  • Open Access


    Intermediary RRT*-PSO: A Multi-Directional Hybrid Fast Convergence Sampling-Based Path Planning Algorithm

    Loc Q. Huynh1, Ly V. Tran1, Phuc N. K. Phan1, Zhiqiu Yu2, Son V. T. Dao1,2,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2281-2300, 2023, DOI:10.32604/cmc.2023.034872

    Abstract Path planning is a prevalent process that helps mobile robots find the most efficient pathway from the starting position to the goal position to avoid collisions with obstacles. In this paper, we propose a novel path planning algorithm–Intermediary RRT*-PSO-by utilizing the exploring speed advantages of Rapidly exploring Random Trees and using its solution to feed to a metaheuristic-based optimizer, Particle swarm optimization (PSO), for fine-tuning and enhancement. In Phase 1, the start and goal trees are initialized at the starting and goal positions, respectively, and the intermediary tree is initialized at a random unexplored region of the search space. The… More >

  • Open Access


    Characterization of Mechanical Properties of CNFs and the Assembled Microfibers Through a Multi-scale Optimization-Based Inversion Method

    Shuaijun Wang1, Wenqiong Tu1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.2, pp. 1-1, 2023, DOI:10.32604/icces.2023.09926

    Abstract Cellulose nanofibrils (CNFs) and the continuously assembled microfibers have shown transversely isotropic behavior in many studies. Due to fact that the size of CNFs and the assembled microfibers is at the nano and micro scale, respectively, the characterization of their mechanical properties is extremely challenge. That greatly hinders the accurate multi-scale modeling and design of CNFs-based materials. In our study, we have characterized the elastic constants of both CNFs microfibers and CNFs through a Multi-scale Optimization Inversion technology. Through the tensile test of CNFs microfibers reinforced resin with different volume fractions and the micromechanics model of microfibers reinforced resin, the… More >

  • Open Access


    Optimal Location and Sizing of Distributed Generator via Improved Multi-Objective Particle Swarm Optimization in Active Distribution Network Considering Multi-Resource

    Guobin He*, Rui Su, Jinxin Yang, Yuanping Huang, Huanlin Chen, Donghui Zhang, Cangtao Yang, Wenwen Li

    Energy Engineering, Vol.120, No.9, pp. 2133-2154, 2023, DOI:10.32604/ee.2023.029007

    Abstract In the framework of vigorous promotion of low-carbon power system growth as well as economic globalization, multi-resource penetration in active distribution networks has been advancing fiercely. In particular, distributed generation (DG) based on renewable energy is critical for active distribution network operation enhancement. To comprehensively analyze the accessing impact of DG in distribution networks from various parts, this paper establishes an optimal DG location and sizing planning model based on active power losses, voltage profile, pollution emissions, and the economics of DG costs as well as meteorological conditions. Subsequently, multi-objective particle swarm optimization (MOPSO) is applied to obtain the optimal… More >

  • Open Access


    Research on Reactive Power Optimization of Offshore Wind Farms Based on Improved Particle Swarm Optimization

    Zhonghao Qian1, Hanyi Ma1, Jun Rao2, Jun Hu1, Lichengzi Yu2,*, Caoyi Feng1, Yunxu Qiu1, Kemo Ding1

    Energy Engineering, Vol.120, No.9, pp. 2013-2027, 2023, DOI:10.32604/ee.2023.028859

    Abstract The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms. To improve the voltage stability and reactive power economy of wind farms, the improved particle swarm optimization is used to optimize the reactive power planning in wind farms. First, the power flow of offshore wind farms is modeled, analyzed and calculated. To improve the global search ability and local optimization ability of particle swarm optimization, the improved particle swarm optimization adopts the adaptive inertia weight and asynchronous learning factor. Taking the minimum active power loss of the offshore wind… More >

  • Open Access


    A PSO Improved with Imbalanced Mutation and Task Rescheduling for Task Offloading in End-Edge-Cloud Computing

    Kaili Shao1, Hui Fu1, Ying Song2, Bo Wang3,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2259-2274, 2023, DOI:10.32604/csse.2023.041454

    Abstract To serve various tasks requested by various end devices with different requirements, end-edge-cloud (E2C) has attracted more and more attention from specialists in both academia and industry, by combining both benefits of edge and cloud computing. But nowadays, E2C still suffers from low service quality and resource efficiency, due to the geographical distribution of edge resources and the high dynamic of network topology and user mobility. To address these issues, this paper focuses on task offloading, which makes decisions that which resources are allocated to tasks for their processing. This paper first formulates the problem into binary non-linear programming and… More >

  • Open Access


    A Productivity Prediction Method Based on Artificial Neural Networks and Particle Swarm Optimization for Shale-Gas Horizontal Wells

    Bin Li*

    FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.10, pp. 2729-2748, 2023, DOI:10.32604/fdmp.2023.029649

    Abstract In order to overcome the deficiencies of current methods for the prediction of the productivity of shale gas horizontal wells after fracturing, a new sophisticated approach is proposed in this study. This new model stems from the combination several techniques, namely, artificial neural network (ANN), particle swarm optimization (PSO), Imperialist Competitive Algorithms (ICA), and Ant Clony Optimization (ACO). These are properly implemented by using the geological and engineering parameters collected from 317 wells. The results show that the optimum PSO-ANN model has a high accuracy, obtaining a R2 of 0.847 on the testing. The partial dependence plots (PDP) indicate that… More >

  • Open Access


    Atrous Convolution-Based Residual Deep CNN for Image Dehazing with Spider Monkey–Particle Swarm Optimization

    CH. Mohan Sai Kumar*, R. S. Valarmathi

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1711-1728, 2023, DOI:10.32604/iasc.2023.038113

    Abstract Image dehazing is a rapidly progressing research concept to enhance image contrast and resolution in computer vision applications. Owing to severe air dispersion, fog, and haze over the environment, hazy images pose specific challenges during information retrieval. With the advances in the learning theory, most of the learning-based techniques, in particular, deep neural networks are used for single-image dehazing. The existing approaches are extremely computationally complex, and the dehazed images are suffered from color distortion caused by the over-saturation and pseudo-shadow phenomenon. However, the slow convergence rate during training and haze residual is the two demerits in the conventional image… More >

  • Open Access


    Fault Diagnosis of Power Electronic Circuits Based on Adaptive Simulated Annealing Particle Swarm Optimization

    Deye Jiang1, Yiguang Wang2,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 295-309, 2023, DOI:10.32604/cmc.2023.039244

    Abstract In the field of energy conversion, the increasing attention on power electronic equipment is fault detection and diagnosis. A power electronic circuit is an essential part of a power electronic system. The state of its internal components affects the performance of the system. The stability and reliability of an energy system can be improved by studying the fault diagnosis of power electronic circuits. Therefore, an algorithm based on adaptive simulated annealing particle swarm optimization (ASAPSO) was used in the present study to optimize a backpropagation (BP) neural network employed for the online fault diagnosis of a power electronic circuit. We… More >

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