Multivector particle swarm optimization algorithm

Fakhouri, Hussam N.; Hudaib, Amjad; Sleit, Azzam

doi:10.1007/s00500-019-04631-x

Cited by 40 publications

(17 citation statements)

References 59 publications

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“…The formula is as follows:

where n is the number of samples, x j is particle swarm optimization, and x i is the risk management of any equipment. The value ranges of projection in each function are {−1, 1} [ 19 ].…”

Section: The Case Study On Risk Control Of Hospital Equipment Managementmentioning

confidence: 99%

[Retracted] A Composite Particle Swarm Optimization Algorithm for Hospital Equipment Management Risk Control Optimization and Prediction

Zhang

2022

Journal of Environmental and Public Health

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Aiming at the problem that particles cannot realize multidimensional analysis and poor global search ability, a composite particle swarm optimization algorithm is proposed, improving the accuracy of particle swarm optimization. Firstly, k-clustering is used to cluster risk management particle swarm optimization. The advantages of particle swarm optimization have to be given full play, and the risk of hospital equipment management from various aspects has to be controlled. Then, the multidimensional particle swarm is segmented to obtain an ordered multidimensional risk particle swarm set, which provides a basis for later risk prediction. Finally, through the fusion function of multidimensional risk particle swarm, the risk particle swarm set based on the clustering degree is constructed, and the optimal extreme value is obtained, so as to improve the accuracy of management risk calculation results. Through MATLAB simulation analysis, it can be seen that the composite particle swarm optimization algorithm is better than particle swarm optimization algorithm in global search accuracy and search time. Moreover, the calculation time and accuracy are better. Therefore, the composite particle swarm optimization algorithm can be used to analyze the risk of hospital equipment and effectively control the risk of hospital equipment management.

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“…The formula is as follows:

where n is the number of samples, x j is particle swarm optimization, and x i is the risk management of any equipment. The value ranges of projection in each function are {−1, 1} [ 19 ].…”

Section: The Case Study On Risk Control Of Hospital Equipment Managementmentioning

confidence: 99%

[Retracted] A Composite Particle Swarm Optimization Algorithm for Hospital Equipment Management Risk Control Optimization and Prediction

Zhang

2022

Journal of Environmental and Public Health

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“…From the velocity update formula and position update formula, it can be seen that each dimension in the search range is independent of each other, so the convergence analysis of the algorithm can be simplified to one dimension [21]. And supposing that, in the population except for the nth one, the rest of the individuals remain motionless, then the behavior of a single individual can be studied, so the subscript n can be omitted.…”

Section: Particle Swarm Cultural Scientific Computingmentioning

confidence: 99%

Construction and Analysis of Urban Cultural Plane Space Mode considering Particle Swarm Cultural Scientific Computing Algorithm

2022

Security and Communication Networks

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In the tide of economic and social internationalization and high-speed urbanization, urban culture has increasingly attracted widespread attention from all walks of life and has a very critical strategic significance in the construction of urban space. This paper aims to construct and analyze the plane space mode of urban culture based on the particle swarm cultural scientific computing algorithm. This article first explains the concept of urban cultural space. Urban cultural space uses urban spatial structure as a media carrier and uses urban cultural time for vertical extension and development; then we proposed the particle swarm cultural scientific calculation algorithm and gave the particle swarm algorithm flowchart; then based on the particle swarm optimization algorithm, the evaluation of the configuration performance of urban cultural facilities is researched and discussed, and at the same time, the evolution law of urban spatial morphology is explored based on the particle swarm optimization algorithm. The particle swarm algorithm is a random search algorithm based on group cooperation developed by simulating the foraging behavior of birds. Urban cultural space is the development of urban space based on urban culture. It plays a special and important role in urban cultural development and urban space planning. According to the statistics of the survey and research results, the utilization rate of city-level cultural facilities in City A is 77, the utilization rate of district-level cultural facilities is 72, the utilization rate of street-community cultural facilities is 69, and the overall evaluation score is 70. It shows that there are significant differences in the actual use of cultural facilities, so it is particularly important to eliminate the differences in the use of cultural facilities between urban and rural areas. In the exploration of the law of urban spatial morphology evolution, it is found that the number of college students, total real estate investment, urban population, and total commercial housing sales have a significant impact on urban spatial expansion and evolution.

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“…The fitting was then performed by minimizing the squared difference between the experimentally measured and calculated transmittance values. The optimal-selection approach of these parameters was carried out using the particle swarm optimization algorithm [27]. After a fine adjustment of the settings, a proper fitting could be obtained, and then the optical constants and thickness of thin film were determined.…”

Section: Single-layer Coatingsmentioning

confidence: 99%

Fabrication of Ultralow Stress TiO2/SiO2 Optical Coatings by Plasma Ion-Assisted Deposition

Guo

Kong

2020

Coatings

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Optical and mechanical properties of multilayer coatings depend on the selected layer materials and the deposition technology; therefore, knowledge of the performances of thin films is essential. In the present work, titanium dioxide (TiO2) and silicon dioxide (SiO2) thin films have been prepared by plasma ion-assisted deposition (PIAD). The optical, structural, and mechanical properties of thin films have been investigated using spectrometer/ellipsometer, X-ray diffraction (XRD), atomic force microscopy (AFM), and laser interferometer. The results show that TiO2 film fabricated by PIAD induces a high refractive index, wide optical band gap, amorphous structure, smooth surface, and tensile stress. In the case of SiO2 film, high bias voltage leads to dense structure and compressive stress. As an application, a three-wavelength high reflectance at 632.8, 808, and 1550 nm was optimized and deposited. The dependence of total stress in the multilayer on the substrate temperature was studied as well. In conclusion, it was demonstrated that PIAD is an effective method for the preparation of ultralow stress TiO2/SiO2 multilayer films. The achieved stress was as low as 1.4 MPa. The result could provide guidance to the stress optimization of most optical components without prefiguring, backside coating, and postdeposition treatments.

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Multivector particle swarm optimization algorithm

Cited by 40 publications

References 59 publications

[Retracted] A Composite Particle Swarm Optimization Algorithm for Hospital Equipment Management Risk Control Optimization and Prediction

[Retracted] A Composite Particle Swarm Optimization Algorithm for Hospital Equipment Management Risk Control Optimization and Prediction

Construction and Analysis of Urban Cultural Plane Space Mode considering Particle Swarm Cultural Scientific Computing Algorithm

Fabrication of Ultralow Stress TiO2/SiO2 Optical Coatings by Plasma Ion-Assisted Deposition

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