Some applications of the PSO for optimization of acoustic filters

Barbieri, Renato; Barbieri, Nilson; Lima, Key Fonseca de

doi:10.1016/j.apacoust.2014.09.007

Cited by 41 publications

(17 citation statements)

References 17 publications

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“…The horn has been used as a model problem in a series of previous studies. [1][2][3][4][5][6][7] Finite element methods on fixed meshes are commonly combined with a level-set description of the geometry, where the boundary is implicitly defined as the zero-level contour of a function, often referred to as the level-set function. Initially used for handling evolving boundaries by Osher and Sethian,8 the level-set method was also adopted for structural optimization, 9 as detailed in several reviews.…”

Section: Introductionmentioning

confidence: 99%

Acoustic shape optimization using cut finite elements

Bernland

Wadbro

Berggren

2017

Numerical Meth Engineering

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Fictitious domain methods are attractive for shape optimization applications, since they do not require deformed or regenerated meshes. A recently developed such method is the CutFEM approach, which allows crisp boundary representations and for which uniformly well-conditioned system matrices can be guaranteed. Here, we investigate the use of the CutFEM approach for acoustic shape optimization, using as test problem the design of an acoustic horn for favorable impedance-matching properties. The CutFEM approach is used to solve the Helmholtz equation, and the geometry of the horn is implicitly described by a level-set function. To promote smooth algorithmic updates of the geometry, we propose to use the nodal values of the Laplacian of the level-set function as design variables. This strategy also improves the algorithm's convergence rate, counteracts mesh dependence, and, in combination with Tikhonov regularization, controls small details in the optimized designs. An advantage with the proposed method is that the exact derivatives of the discrete objective function can be expressed as boundary integrals, as opposed to when using a traditional method that uses mesh deformations. The resulting horns possess excellent impedance-matching properties and exhibit surprising subwavelength structures, not previously seen, which are possible to capture due to the fixed mesh approach.

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Section: Introductionmentioning

confidence: 99%

Acoustic shape optimization using cut finite elements

Bernland

Wadbro

Berggren

2017

Numerical Meth Engineering

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“…Finally, the maximum number of iterations is 100, adopted from [33]. We work with three types of environments: (1) 32 servers, (2) 16 servers, and (3) 8 servers.…”

Section: Experiments Setupmentioning

confidence: 99%

Performance-aware placement and chaining scheme for virtualized network functions: a particle swarm optimization approach

et al. 2021

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Network functions virtualization (NFV) is a new concept that has received the attention of both researchers and network providers. NFV decouples network functions from specialized hardware devices and virtualizes these network functions as software instances called virtualized network functions (VNFs). NFV leads to various benefits, including more flexibility, high resource utilization, and easy upgrades and maintenances. Despite recent works in this field, placement and chaining of VNFs need more attention. More specifically, some of the existing works have considered only the placement of VNFs and ignored the chaining part. So, they have not provided an integrated view of host or bandwidth resources and propagation delay of paths. In this paper, we solve the VNF placement and chaining problem as an optimization problem based on the particle swarm optimization (PSO) algorithm. Our goal is to minimize the required number of used servers, the average propagation delay of paths, and the average utilization of links while meeting network demands and constraints. Based on the obtained results, the algorithm proposed in this study can find feasible and high-quality solutions.

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“…PSO is based on the paradigm of swarm intelligence, and it is inspired by the social behaviors of animals such as fish and birds. It is a simple but powerful optimization algorithm and is successfully used in numerous engineering applications such as modelling, signal processing, and robotics along with machine learning, data processing, and many other techniques [56][57][58].…”

Section: Particle Swarm Optimization Algorithm (Pso)mentioning

confidence: 99%

Robust Control for Non-Minimum Phase Systems with Actuator Faults: Application to Aircraft Longitudinal Flight Control

et al. 2021

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This study is concerned with developing a robust tracking control system that merges the optimal control theory with fractional-order-based control and the heuristic optimization algorithms into a single framework for the non-minimum phase pitch angle dynamics of Boeing 747 aircraft. The main control objective is to deal with the non-minimum phase nature of the aircraft pitching-up action, which is used to increase the altitude. The fractional-order integral controller (FIC) is implemented in the control loop as a pre-compensator to compensate for the non-minimum phase effect. Then, the linear quadratic regulator (LQR) is introduced as an optimal feedback controller to this augmented model ensuring the minimum phase to create an efficient, robust, and stable closed-loop control system. The control problem is formulated in a single objective optimization framework and solved for an optimal feedback gain together with pre-compensator parameters according to an error index and heuristic optimization constraints. The fractional-order integral pre-compensator is replaced by a fractional-order derivative pre-compensator in the proposed structure for comparison in terms of handling the non-minimum phase limitations, the magnitude of gain, phase-margin, and time-response specifications. To further verify the effectiveness of the proposed approach, the LQR-FIC controller is compared with the pole placement controller as a full-state feedback controller that has been successfully applied to control aircraft dynamics in terms of time and frequency domains. The performance, robustness, and internal stability characteristics of the proposed control strategy are validated by simulation studies carried out for flight conditions of fault-free, 50%, and 80% losses of actuator effectiveness.

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Some applications of the PSO for optimization of acoustic filters

Cited by 41 publications

References 17 publications

Acoustic shape optimization using cut finite elements

Acoustic shape optimization using cut finite elements

Performance-aware placement and chaining scheme for virtualized network functions: a particle swarm optimization approach

Robust Control for Non-Minimum Phase Systems with Actuator Faults: Application to Aircraft Longitudinal Flight Control

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