Improved Dynamical Particle Swarm Optimization Method for Structural Dynamics

Annicchiarico, W.; Cerrolaza, M.

doi:10.1155/2019/8250185

Cited by 6 publications

(5 citation statements)

References 35 publications

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“…Since this study mainly investigates the thermal fatigue life of the block brake, the minimum block brake mass and the maximum brake wheel thermal fatigue life were considered objective functions under certain constraints, and a mathematical model for block brake structural optimization was established. Simultaneously, the multi-objective genetic algorithm was combined to achieve the optimal block brake design [ 17 ].…”

Section: Determination Of Block Brake Optimization Objectives and Des...mentioning

confidence: 99%

Elevator block brake structural optimization design based on an approximate model

Wang,

Yu,

Zhu

et al. 2024

PLoS ONE

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An Aquila optimizer-back propagation (AO-BP) neural network was used to establish an approximate model of the relationship between the design variables and the optimization objective to improve elevator block brake capabilities and achieve a lightweight brake design. Subsequently, the constraint conditions and objective functions were determined. Moreover, the multi-objective genetic algorithm optimized the structural block brake design. Finally, the effectiveness of the optimization results was verified using simulation experiments. The results demonstrate that the maximum temperature of the optimized brake wheel during emergency braking was 222.09°C, which is 36.71°C lower than that of 258.8°C before optimization, with a change rate of 14.2%. The maximum equivalent stress after optimization was 246.89 MPa, 28.87 MPa lower than that of 275.66 MPa before optimization, with a change rate of 10.5%. In addition, the brake wheel mass was reduced from 58.85 kg to 52.40 kg, and the thermal fatigue life at the maximum equivalent stress increased from 64 times before optimization to 94 times after optimization.

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Section: Determination Of Block Brake Optimization Objectives and Des...mentioning

confidence: 99%

Elevator block brake structural optimization design based on an approximate model

Wang,

Yu,

Zhu

et al. 2024

PLoS ONE

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“…There are research employing modified PSO for modeling of multi-story shear building subjected to seismic motion. 20 Other researchers developed an improved PSO for subsea oil pipeline optimization, 21 but not using the FE approach. In other hand, there are works that develop the FE-PSO approach for structure optimization.…”

Section: Introductionmentioning

confidence: 99%

Numerical approach based on particle swarm technique for optimization of nonlinear buried pipeline on tensionless foundation

Shang,

Deitos,

Ulrich

2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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This work aims to contribute to the development of numerical approach for optimization of buried pipeline by using finite element (FE) method and particle swarm optimization (PSO), considering static analysis and material hardening. The buried pipeline is discretized by finite element approach, constituted by Euler-Bernoulli three nodes beam element, while the soil-structure interaction is modeled by Winker-type tensionless foundation. Moreover, the tensionless foundation is discretized by spring element in the numerical model. The material nonlinearity behavior is analyzed by Von Mises isotropic hardening model. In the numerical approach, this model is solved by stress increment procedure. Furthermore, the critical loading and optimum wall thickness, that leads to optimized condition, are determined using swarm techniques. The cost function is formulated using evaluated stress and target stress, more, the calculated error is minimized by using swarm techniques for single-variable and two-variable optimization. Several applications are carried out by considering several soil conditions, boundary conditions and loading types. As a novelty, the soil random stiffness is included in the numerical model, where the optimum wall thickness and critical loading are evaluated considering material nonlinearity. The performance of finite element particle swarm technique is also compared to finite element genetic algorithms technique. The results demonstrate the effectiveness of proposed numerical approach for nonlinear buried pipeline optimization.

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“…Either with increment or minimisation, it is the true operation of the simplest state while constantly screening the input elements associated with an allowed set of accessible alternatives [8]. In the field of structural engineering, PSO is one of the evolutionary computation (EC) techniques used by many researchers [9]. PSO was proposed by [5] in 1995, and it contains memory that allows each member of the swarm to remember and acquire while moving through the searching space [9].…”

Section: Introductionmentioning

confidence: 99%

“…In the field of structural engineering, PSO is one of the evolutionary computation (EC) techniques used by many researchers [9]. PSO was proposed by [5] in 1995, and it contains memory that allows each member of the swarm to remember and acquire while moving through the searching space [9]. PSO is inspired by social problem-solving and seeks to perform a parallel search for the optimal solution with many individual searches associated with particles and collaborative influencing by joining the best performance of all searches [10].…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimisation for Emotion Recognition Systems: A Decade Review of the Literature

et al. 2023

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Particle Swarm Optimisation (PSO) is a popular technique in the field of Swarm Intelligence (SI) that focuses on optimisation. Researchers have explored multiple algorithms and applications of PSO, including exciting new technologies, such as Emotion Recognition Systems (ERS), which enable computers or machines to understand human emotions. This paper aims to review previous studies related to PSO findings for ERS and identify modalities that can be used to achieve better results through PSO. To achieve a comprehensive understanding of previous studies, this paper will adopt a Systematic Literature Review (SLR) process to filter related studies and examine papers that contribute to the field of PSO in ERS. The paper’s primary objective is to provide better insights into previous studies on PSO algorithms and techniques, which can help future researchers develop more accurate and sustainable ERS technologies. By analysing previous studies over the past decade, the paper aims to identify gaps and limitations in the current research and suggest potential areas for future research. Overall, this paper’s contribution is twofold: first, it provides an overview of the use of PSO in ERS and its potential applications. Second, it offers insights into the contributions and limitations of previous studies and suggests avenues for future research. This can lead to the development of more effective and sustainable ERS technologies, with potential applications in a wide range of fields, including healthcare, gaming, and customer service.

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Improved Dynamical Particle Swarm Optimization Method for Structural Dynamics

Cited by 6 publications

References 35 publications

Elevator block brake structural optimization design based on an approximate model

Elevator block brake structural optimization design based on an approximate model

Numerical approach based on particle swarm technique for optimization of nonlinear buried pipeline on tensionless foundation

Particle Swarm Optimisation for Emotion Recognition Systems: A Decade Review of the Literature

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