The rapid development of time-sensitive applications, such as autopilot, telemedicine and industrial automation, demands more stringent requirements on end-to-end delay. Therefore, low-delay planning of 5G networks is...
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The rapid development of time-sensitive applications, such as autopilot, telemedicine and industrial automation, demands more stringent requirements on end-to-end delay. Therefore, low-delay planning of 5G networks is supposed to be a crucial stage. In this paper, a low-delay layout planning based on the nonlinear decreasing inertia weight particle swarm optimization algorithm (NL-wPSO) is demonstrated. By iteratively calculating the velocity and position of each particle, the proposed algorithm can optimize the layout of AAUs in the fronthaul network. Thereby, the distance between the AAUs and edge computing server will be shortened. Compared to the basic PSO, the simulation results indicate a significant reduction on transmission delay.
Due to the Doppler broadening effect, the conventional subgroup weights of different temperatures correspond to different energy ranges. Therefore, the conventional subgroup fixed source equation could not be establis...
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Due to the Doppler broadening effect, the conventional subgroup weights of different temperatures correspond to different energy ranges. Therefore, the conventional subgroup fixed source equation could not be established without approximation between different temperatures. In this study, the improved subgroup method coupled with the particle swarm optimization algorithm (ISPSO) is proposed. The subgroup fixed source equation is solved for different temperatures by sharing the same set of subgroup weights, which are calculated by generationally updating the particle position during the particleswarmoptimization. Afterward, the partial subgroup levels are obtained by the least square method. Besides, ISPSO adopts the fine mesh structure for the resonance interference treatment, then the multigroup effective resonance cross section is obtained by the group condensation. The subgroup parameters of ISPSO could be either generated online or pre-determined. The numerical verification indicates that ISPSO could accurately handle the resonance treatment for the complicated temperature conditions. (C) 2020 Elsevier Ltd. All rights reserved.
As the energy shortage and environment pollution become increasingly deteriorating, remanufacturing has become a hot research field for its energy-saving and environmental advantages. In remanufacturing systems, sched...
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As the energy shortage and environment pollution become increasingly deteriorating, remanufacturing has become a hot research field for its energy-saving and environmental advantages. In remanufacturing systems, scheduling is a key step that directly affects the successful realization of remanufacturing benefits. Since the remanufacturing involves many two-fold uncertainties, scheduling for remanufacturing is more challenging than for traditional manufacturing. However, few studies have addressed these two-fold uncertainties in remanufacturing environments. Thus, this study proposes a new bifuzzy remanufacturing scheduling model, which handles many two-fold uncertainties in remanufacturing and applies the bifuzzy variable to describe these uncertainties. Multiple non-identical parallel processing lines for remanufacturing end-of-life products have also been integrated into the proposed model. An extended discrete particle swarm optimization algorithm with an effective representation scheme is proposed to solve the model. In the presented algorithm, three new guiding directions and a new multi-directional guiding strategy are designed to enhance the diversity of population, and a new position updating mechanism, local search strategy, and anti-stagnation mechanism are integrated to improve the algorithmic performance. Experiments are performed to verify the effectiveness of the proposed model by comparing it with the traditional deterministic model, and the effectiveness of the proposed algorithm in solving this model by comparing it with other baseline algorithms.
The improvement of enterprise competitiveness depends on the ability to match segmented customers in a competitive market. In this study, we propose a customer segmentation method based on the improved K-means algorit...
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The improvement of enterprise competitiveness depends on the ability to match segmented customers in a competitive market. In this study, we propose a customer segmentation method based on the improved K-means algorithm and the adaptive particleswarmoptimization (PSO) algorithm. The current PSO algorithm can easily fall into a local extremum;thus, adaptive learning PSO (ALPSO) is proposed to improve the optimization accuracy. On the basis of the analysis of population-based optimization, the inertia weight, learning factors, and the position update method are redesigned. To prevent the K-means clustering algorithm from depending on initial cluster centres, the ALPSO algorithm is used to optimize the K-means cluster centres (KM-ALPSO). Aimed at the issue of clustering the actual grape-customer consumption mixed dataset, factor analysis is used to extract numerical variables. We then propose a dissimilarity measurement method to cluster the mixed data. We compare ALPSO with several parameter update methods. We also conduct comparative experiments to compare KM-ALPSO on five UCI datasets. Finally, the improved KM-ALPSO (IKM-ALPSO) clustering algorithm is applied in customer segmentation. All results show that the three proposed methods outperform existing models. The experimental results also demonstrate the effectiveness and practicability of IKM-ALPSO for customer segmentation. (C) 2021 Elsevier B.V. All rights reserved.
The accommodation of high-penetration renewable power poses a considerable challenge to power grids. Coal-fired combined heat and power (CHP) stations are forced to enhance their operational flexibility by applying he...
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The accommodation of high-penetration renewable power poses a considerable challenge to power grids. Coal-fired combined heat and power (CHP) stations are forced to enhance their operational flexibility by applying heat-power decoupling technologies. Power-to-heat devices, including electric boilers and heat pumps, are capable to enhance the operational flexibility of coal-fired CHP stations. The problem regarding the operation scheduling of a CHP station with multiple CHP units and power-to-heat devices is addressed in this study. Operation optimization models integrated with detail CHP unit models are developed, and the particle swarm optimization algorithm is utilized as the optimizationalgorithm. Then, a case study are carried out. Results show that the unequal distribution of heating and power loads among coal-fired CHP units can decrease the total irreversibility caused by heating steam pressure regulation. The operation scheduling method provided in this study can decrease the total coal consumption by 14.14 and 14.70 t/day for the CHP station integrated with an electric boiler and a heat pump, respectively. As a result, 1204.7 and 1252.44 ton CO2, and an additional similar to 182 and-190 kUSD/year can be saved for the reference CHP station integrated with an electric boiler and a heat pump, respectively. (C) 2020 Elsevier Ltd. All rights reserved.
Quantum particleswarmalgorithm integrated the quantum behavior with particle swarm optimization algorithm, is used to settle the majorization question of calculating available transmission capability. And by using t...
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ISBN:
(数字)9781728197241
ISBN:
(纸本)9781728197241
Quantum particleswarmalgorithm integrated the quantum behavior with particle swarm optimization algorithm, is used to settle the majorization question of calculating available transmission capability. And by using the software of Matlab to IEEE-30 bus system as an example of the simulation, after comparing the simulation results with the traditional particle swarm optimization algorithm results, we dissected the optimization performance and convergence speed of the above two algorithms, and verify the effectiveness of quantum particleswarmalgorithm to settle the majorization question of the available transmission capability.
The particleswarmoptimization is a classical optimizationalgorithm (PSO) that has been applied to various fields. It implements simple but efficient evolution operators to search for optimums in parameter space. Ho...
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The particleswarmoptimization is a classical optimizationalgorithm (PSO) that has been applied to various fields. It implements simple but efficient evolution operators to search for optimums in parameter space. However, it could not reach a good balance between global search and local search when it comes to multimodal' problem. Levy flight is a kind of stochastic process with scale invariant, turned out to accord to animals' behavior. This paper utilizes the levy flight operator to enhance the PSO's global search ability during the iteration, which is based on the hierarchy structure of swarms. Therefore, a novel competitive particleswarmoptimization based on levy flight (CLFPSO) is proposed. A number of benchmarks has been tested on the CLPSO with other five typical PSO algorithms. The experimental results show that the proposed algorithm could reach more outstanding and accurate consequences compared with other algorithms.
Fastening clips, though seemingly minor components, are crucial for maintaining the stability and smoothness of railway tracks. However, clip fatigue fractures have been frequently encountered in high-speed railways d...
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Fastening clips, though seemingly minor components, are crucial for maintaining the stability and smoothness of railway tracks. However, clip fatigue fractures have been frequently encountered in high-speed railways due to high-frequency excitations. This study develops an efficient predictive model for clip fatigue life assessment using a particle swarm optimization algorithm (PSOA). Firstly, the central axis of an omega-type fastening clip with spatially variable curvature is mathematically described via ten independent parameters. By employing the modal superposition method (MSM) and PSOA, we have derived vibration equations of a prestressed clip. This approach addresses nonlinear contact through simplified constraints, reduces degrees of freedom, and achieves faster numerical convergence than traditional 3D finite element (FE) models, thereby enabling efficient prediction of fatigue life due to vehicle-track dynamic interactions. The model's reliability has been validated by existing numerical results and experimental studies. Subsequently, high vibration, high stress, and low fatigue life regions, as well as the specific location most prone to fracture, are identified based on the spatial distribution of clip dynamic responses. The potential causes of stress concentration at the critical location are explored by examining the clip's geometric characteristics, including curvature, torsion, and their derivatives. Finally, control thresholds for the amplitude of short-wavelength irregularities and vertical vibration displacement of clips are proposed to meet the fatigue life requirement. The current work provides guidance for the maintenance and management of high-speed railway fastening clips and inspires the optimization of the clip configuration.
In recent years, wireless sensor networks localization becomes a crucial method in the indoor positioning. Following the frontiers of technology, we studied on ZigBee wireless sensor network. Since the parameters of t...
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In recent years, wireless sensor networks localization becomes a crucial method in the indoor positioning. Following the frontiers of technology, we studied on ZigBee wireless sensor network. Since the parameters of the path loss model are difficult to be estimated by the ordinary methods, the particleswarmoptimization (PSO) method is proposed in this paper to simulate the parameter estimation in the indoor environment. The Texas Instruments CC2530 chip was also taken to build a ZigBee wireless sensor network. The data collected from ZigBee wireless sensor network experiment could be used to estimate the model parameters. PSO algorithm for fitting the signal attenuation curve removed the poor experimental data, and the output model fit well with the signal attenuation curve. Experimental results demonstrate that the PSO algorithm works well, clear, easy to understand, and has a high reliability. Using the parametric model to locate the user's position, and with the weighted K-Nearest Neighbor algorithm, the two-dimensional (2D) positioning was improved effectively. The standard deviation of 2D positioning is 1.15 m, so the model has practical value. Through the experiment and analyzing the data, it is verified that the proposed PSO algorithm in this paper is better than the previous methods we presented. (C) 2018 Elsevier Ltd. All rights reserved.
An efficient hybrid genetic algorithm and particle swarm optimization algorithm (HGAPSO) is studied in this work for load balancing of molecular dynamics simulations (MDS) on heterogeneous supercomputers by combining ...
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An efficient hybrid genetic algorithm and particle swarm optimization algorithm (HGAPSO) is studied in this work for load balancing of molecular dynamics simulations (MDS) on heterogeneous supercomputers by combining the genetic algorithm (GA) and the particleswarmoptimization (PSO) algorithm. A hybrid CPU-GPU platform is applied to enabling large-scale MDS that emulates the metal solidification. Applied to task scheduling of the parallel algorithm, the approach obtains excellent results. The experimental results show that the proposed algorithm can improve the efficiency of parallel computing and the precision of physical simulation. (C) 2018 Elsevier B.V. All rights reserved.
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