Curve-Fitting on Graphics Processors Using Particle Swarm Optimization

Kneusel, Ronald T.

doi:10.1080/18756891.2013.869901

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…One way to profit from such powerful hardware is to run multiple swarms simultaneously. This kind of implementation can be seen in [24,47,[47][48][49][50][51][52][53][54][55][56]. SI algorithms have a non-deterministic behavior, therefore each run typically produces different results.…”

Section: Parallel and Cooperative Swarmsmentioning

confidence: 99%

Parallelization of Swarm Intelligence Algorithms: Literature Review

Menezes

Kuchen²,

Neto³

2022

Int J Parallel Prog

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Swarm Intelligence (SI) algorithms are frequently applied to tackle complex optimization problems. SI is especially used when good solutions are requested for NP hard problems within a reasonable response time. And when such problems possess a very high dimensionality, a dynamic nature, or present intrinsic complex intertwined independent variables, computational costs for SI algorithms may still be too high. Therefore, new approaches and hardware support are needed to speed up processing. Nowadays, with the popularization of GPU and multi-core processing, parallel versions of SI algorithms can provide the required performance on those though problems. This paper aims to describe the state of the art of such approaches, to summarize the key points addressed, and also to identify the research gaps that could be addressed better. The scope of this review considers recent papers mainly focusing on parallel implementations of the most frequently used SI algorithms. The use of nested parallelism is of particular interest, since one level of parallelism is often not sufficient to exploit the computational power of contemporary parallel hardware. The sources were main scientific databases and filtered accordingly to the set requirements of this literature review.

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Section: Parallel and Cooperative Swarmsmentioning

confidence: 99%

Parallelization of Swarm Intelligence Algorithms: Literature Review

Menezes

Kuchen²,

Neto³

2022

Int J Parallel Prog

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“…We apply PSO algorithm [29][30][31] to determine coordinates of control points of Bezier curve, as shape of Bezier curve is completely determined by its control point. PSO algorithm, compared with other advanced optimal algorithms like genetic algorithm and simulated annealing algorithm, is more robust and timesaving in terms of relatively small data sets, as its inherent process is more concise.…”

Section: Bounce Mechanismmentioning

confidence: 99%

Models of a hybrid wheeled hopping self-balanced robot

Zeng

Bai

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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Combining wheeled structure with hopping mechanism, this paper purposes a self-balanced hopping robot with hybrid motion pattern. The main actuator which is the cylindrical cam, optimized by particle swarm optimization (PSO), is equipped with the motor to control the hopping motion. Robotic system dynamics model is established and solved by Lagrangian method. After linearization, control characteristics of the system is obtained by classical control theory based on dynamics equations. By applying Adams and Matlab to simulate the system, hopping locomotion and self-balanced capability are validated respectively, and result shows that jump height can reach 750 mm theoretically. Then PID control scheme is developed and specific models of hardware and software are settled down accordingly. Finally, prototype is implemented and series of hopping experiments are conducted, showing that with different projectile angle, prototype can jump 550 mm in height and 460 mm in length, transcending majority of other existing hopping robots.

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“…ICA and PSO [5][6][7] are evolutionary optimization methods which have shown great performance in the global optimum achievement. In the present work, ICA and PSO are employed to optimize the initial weights of ANN.…”

Section: Introductionmentioning

confidence: 99%

Continuous Prediction of the Gas Dew Point Temperature for the Prevention of the Foaming Phenomenon in Acid Gas Removal Units Using Artificial Intelligence Models

Rohani¹,

Jazayeri-Rad²,

Behbahani³

2017

IJCIS

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Acid gas removal (AGR) units are widely used to remove CO 2 and H 2 S from sour gas streams in natural gas processing. When foaming occurs in an AGR system, the efficiency of the process extremely decreases. In this paper, a novel approach is suggested to regularly predict the gas dew point temperature (GDPT) in order to anticipate the foaming conditions. Prediction of GDPT is advantageous because the conventional methods of measuring GDPT such as: (i) using a chilled mirror device is time consuming; and (ii) the use of gas chromatograph for composition determination combined with the equation-of-state calculations involve column retention time and is expensive. New hybrid modeling algorithms based on the artificial neural network (ANN) combined with either the imperialist competitive algorithm (ICA) or particle swarm optimization (PSO) are employed to model the process. The models can then be used to prevent the foaming phenomenon. The proposed algorithms combine the local searching ability of ANN with the global searching abilities of ICA and PSO. ICA and PSO are used to optimize the initial weights of the neural networks. The resulting ICA-ANN and PSO-ANN combined algorithms are then applied to model the occurrence of foaming in the AGR plant based on a simulation data set acquired from the 6 th refinery of the south Pars gas complex in Iran. The performances of the ICA-ANN, PSO-ANN and conventional ANN models are then compared against each other. It was found that the accuracies of the ICA-ANN and PSO-ANN models are better than that of the conventional ANN model. In addition, the PSO-ANN model outperformed the ICA-ANN model.

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Curve-Fitting on Graphics Processors Using Particle Swarm Optimization

Cited by 6 publications

References 13 publications

Parallelization of Swarm Intelligence Algorithms: Literature Review

Parallelization of Swarm Intelligence Algorithms: Literature Review

Models of a hybrid wheeled hopping self-balanced robot

Continuous Prediction of the Gas Dew Point Temperature for the Prevention of the Foaming Phenomenon in Acid Gas Removal Units Using Artificial Intelligence Models

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