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Diamantis, Z.G.; Tsahalis, D. T.; Borchers, I. U.

doi:10.1023/a:1019924707917

Cited by 15 publications

(2 citation statements)

References 9 publications

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“…The performance characteristics of various genetic operators have been thoroughly tested for the optimal search configuration of a cylinder with a floor structure, representative of an aircraft fuselage [16]. The application of genetic algorithms for the optimal placement of sources and microphones inside an aircraft cabin has been studied [17]. In [18], the authors have compared natural algorithms with optimization techniques based on gradient descent methods, for the optimal secondary source locations in an ANC system of a pure tone enclosed field, finding that the former are more efficient and robust.…”

Section: Introductionmentioning

confidence: 99%

Active Low-Frequency Noise Control Implementing Genetic Algorithm on Mode Coupling of a Compound Source

2023

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A system for low-frequency noise control in small, enclosed sound fields is proposed, using compound sound sources optimized by a genetic algorithm (GA). It is the integration of the developed low-Bl driver compound sources with a GA computer program in the Python language, aiming to control the modal field. The lack of appropriate free space in small rooms is critical for positioning the secondary sound sources; therefore, the proposed system has been designed to adapt to any available position. Two quadrupole topologies of the secondary compound source are applied and examined in a room. The convergence of the algorithm to the optimal solutions is attained through parametric configuration. The spatial radiation of the compound source at a single fixed position is adapted to couple with the modal noise field and attenuate it. The experimental results indicate that the proposed system can successfully control resonances of different low frequencies down to 50 Hz at multiple positions. The tonal noise attenuation reaches up to 32 dB at 100 Hz, confirming the applicability of the small subwoofer loudspeaker configurations for low-frequency control. This new method offers a practical and effective alternative to the typical abatement techniques that use distributed monopole sources in limited spaces.

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Section: Introductionmentioning

confidence: 99%

Active Low-Frequency Noise Control Implementing Genetic Algorithm on Mode Coupling of a Compound Source

2023

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“…For example, in [7], different nonlinear optimization techniques are used and compared, including genetic algorithms, for simultaneous optimization of the positions of loudspeakers and microphones in a doublepanel partition. The application of genetic algorithms for optimal placement of sensors and actuators inside confined spaces is studied in [8,18]. The proposed genetic algorithms include the multivariable binary coding (MVBS) as well as the integer coding scheme.…”

Section: Introductionmentioning

confidence: 99%

The use of ‘particle swarm’ to optimize the control system in a PZT laminated plate

Montazeri

Poshtan

Yousefi‐Koma

2008

Smart Mater. Struct.

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In many active noise and vibration control systems it is desired to reduce the vibration and also the noise emitted by flexible structures. With this objective the task of the controller is to control the maximum number of modes allowed by the control system's limitations. Some of the key parameters in the control system design for this purpose are the location, number and size of actuators and sensors on a flexible structure. In this paper a simply-supported thin plate with laminated piezoelectric sensors and actuators is studied for noise and vibration attenuation. For this purpose, a performance index based on Hankel singular values of the system is selected. The resulting nonlinear optimization problem is solved using a new particle swarm optimization (PSO) algorithm. The results are compared with a genetic algorithm and verified with a simple state-feedback controller.

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Modified imperialist competitive algorithm for aircraft landing scheduling problem

Shirini,

Aghdasi,

Saeedvand

2024

J Supercomput

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Cited by 15 publications

References 9 publications

Active Low-Frequency Noise Control Implementing Genetic Algorithm on Mode Coupling of a Compound Source

Active Low-Frequency Noise Control Implementing Genetic Algorithm on Mode Coupling of a Compound Source

The use of ‘particle swarm’ to optimize the control system in a PZT laminated plate

Modified imperialist competitive algorithm for aircraft landing scheduling problem

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