Study on Form-Finding of Cable-Membrane Structures Based on Particle Swarm Optimization Algorithm

Sun, Fenjin; Zhu, Donghan; Liang, Meirong; Zhang, Daming

doi:10.1155/2020/1281982

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Looking at the current literature on the performance appraisal of counselors' daily work, most of the research focuses on the analysis of the current status of the performance appraisal system for counselors in colleges and universities. Reasonable and effective performance appraisal system puts forward its own countermeasures and suggestions [13][14][15]. Landy et al believe that performance evaluation is the central function of human resource management because it is a test of the content and early input effects of many human resource management activities [16].…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Particle Swarm Optimisation Method for Performance Evaluation of Instructors

Yuanhong

2022

Security and Communication Networks

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Performance evaluation of counselors plays a vital role for education industry (schools, colleges, universities, and vocational colleges). The problem can be stated with the phrases; how to design reasonable and appropriate performance indicators? The objective of this research is to design an effective performance evaluation. The purpose of this study is to explore a new method of performance evaluation that combines strategic goals with personal development goals. The purpose of performance evaluation is to better motivate the enthusiasm of counselors. With the methodology, a new issue faced at modern colleges and universities is being resolved. Therefore, for explaining methodology, this study has carried out the application analysis of the fusion particle swarm algorithm (FPSA) in the performance evaluation of instructors. First, on the basis of comprehensive analysis of performance evaluation, it discusses the advantages and disadvantages of university performance evaluation. Secondly, particle swarm and fuzzy comprehensive evaluation methods are used in the research of instructor performance evaluation. Pass the superiority of this assessment system. Index parameter evaluation is from 2.5 to 3.0. The range indicates an excellent value. In result this improved particle swarm can be compared with the state of the art (Liu et al., 2019). In conclusive remarks, this study is to provide state-of-the-art study for the current research on the topic of instructor performance appraisal in colleges and universities.

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Section: Literature Reviewmentioning

confidence: 99%

An Improved Particle Swarm Optimisation Method for Performance Evaluation of Instructors

Yuanhong

2022

Security and Communication Networks

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“…In the PSO algorithm, the potential solutions to the considered optimization problem are called particles and fly through the solution domain. Particle swarm optimization is a stochastic optimization technique derived from swarm intelligence, compared with the genetic algorithm [26].…”

Section: Methodsmentioning

confidence: 99%

“…. rough the analysis and summary, the position of each particle (that is, where each individual bird is in the food-seeking space) is changed according to the following formula ("flight") [26]:…”

Section: Methodsmentioning

confidence: 99%

Optimization Design of Wind Turbine Blade Based on an Improved Particle Swarm Optimization Algorithm Combined with Non‐Gaussian Distribution

Sun

2021

Advances in Civil Engineering

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To overcome the problem of particle swarm optimization (PSO) being trapped in local minima, a particle swarm optimization algorithm combined with non-Gaussian stochastic distribution is presented for optimization design of wind turbine blade. Before updating the particle velocity, a limited test was performed for every particle to search for the global best solution. Taking the maximum wind turbine annual power generation as the final objective, a 1.3 MW wind turbine blade was optimized. The results were compared with those of the original wind turbine blades and traditional particle swarm optimization. Compared with the original output power, the year output power increased by 5.3% with non-Gaussian stochastic distribution combined with PSO, whereas the computation time was 65% of the traditional PSO. As time step increased, residuals of non-Gaussian stochastic distribution combined with PSO greatly diminished, with improved computation efficiency. It is shown that the non-Gaussian distribution combined with PSO ensures the global best solution. The non-Gaussian distribution combined with PSO provides a more reliable theoretical basis for the design of wind turbine blades.

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Research on building truss design based on particle swarm intelligence optimization algorithm

Sun

Shabaz

et al. 2021

Int J Syst Assur Eng Manag

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Optimization methodologies are being utilized in various structural designing practices to solve size, shape and topology optimization problems. A heuristic Particle swarm optimization (HPSO) algorithm was anticipated in this article in order to address the size optimization problem of truss with stress and displacement constraints. This article contributes in improvisation in the truss structure design rationality while reducing the engineering cost by proposing the HPSO approach. Primarily, the basic principle of the original PSO algorithm is presented, then the compression factor is established to improve the PSO algorithm, and a reasonable parameter setting value is presented. To validate the performance of the proposed optimization approach, various experimental illustrations were performed. The results show that the convergence history of experimental illustration 2 and experimental illustration 3 is optimal. The experimental illustration 2 converges after about 150 iterations, however, the experimental illustration 3 is close to the optimal solution after about 500 iterations. Therefore, the PSO algorithm can successfully optimize the size design of truss structures, and the algorithm is also time e cient. The improved PSO algorithm has good convergence and stability, and can effectively optimize the size design of truss structures.

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Study on Form-Finding of Cable-Membrane Structures Based on Particle Swarm Optimization Algorithm

Cited by 5 publications

References 9 publications

An Improved Particle Swarm Optimisation Method for Performance Evaluation of Instructors

An Improved Particle Swarm Optimisation Method for Performance Evaluation of Instructors

Optimization Design of Wind Turbine Blade Based on an Improved Particle Swarm Optimization Algorithm Combined with Non‐Gaussian Distribution

Research on building truss design based on particle swarm intelligence optimization algorithm

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