Acceleration of Particle Swarm Optimization with AVX Instructions

Šafařík, Jakub; Snášel, Václav

doi:10.3390/app13020734

Cited by 2 publications

(2 citation statements)

References 28 publications

(31 reference statements)

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“…It can be based on the dynamic structure of known data samples, fully consider the relevant characteristics of variables within the value range and analyze the trends and dynamics of known data samples. A good fit for nonlinear problems between the response variable and the design variable [14,15]. The Kriging model includes both regression and a nonparametric part.…”

Section: Kriging Modelmentioning

confidence: 99%

Hydrodynamic Characteristic Analysis and NSGA-II Optimization of a Vacuum Fish Pump

Hong

Zhu

Zhang

et al. 2023

JMSE

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The fish pump is an important piece of power equipment for aquaculture, especially for deep-sea engineering vessels and cage culture. Fish pump research focuses on reducing fish body damage, improving survival rates, and increasing fish lifting efficiency. The research object in this paper is a new type of vacuum fish pump, with the aim of improving the hydraulic performance of the vacuum fish pump and reducing the damage to the fish body. The dependent variables include the dynamic change process of the flow state and flow field under diachronic conditions, the fluid simulation analysis of the vacuum pump body and the flow channel structure, the inlet flow rate of the fish pump, the negative pressure of the pipeline, and the impact force of the water flow on the inner wall of the tank. The independent variables include the operating conditions of the pump body and the fish pump. The Latin hypercube sampling method is used to extract 167 sets of calculation models for the independent variables, and multi-objective optimization is performed based on the NSGA-II algorithm for the hydrodynamic performance of the fish pump. On the basis of ensuring the fish body damage rate, the structural parameters of the vacuum fish pump with the optimal hydrodynamic performance under 167 sets of parameter values were obtained. The optimized parameters were then entered into the solver again, and the results showed that, in the optimal structural parameters under certain conditions, the direction of the incident water flow in the vacuum fish pump tank is close to the upper end of the tank body, which will reduce the speed of the fish-water mixed flow when entering the tank, thereby reducing the collision damage to the fish body. Currently, the water flow velocity at the water inlet is about 2.5 m/s, and the negative pressure value distribution gradient between the tank body and the water inlet pipeline is quite consistent, which can achieve good fish suction and fish lifting effects.

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Section: Kriging Modelmentioning

confidence: 99%

Hydrodynamic Characteristic Analysis and NSGA-II Optimization of a Vacuum Fish Pump

Hong

Zhu

Zhang

et al. 2023

JMSE

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“…Particle swarm optimization (PSO) is a well-established swarm optimization algorithm that has proven to provide high performance in many application areas which require that optimization problems be solved [1][2][3]. In the engineering fields, it has been applied in robotics [4], power systems [5,6] solar energy systems [7] image processing [8][9][10] control [11,12], wireless communication networks [13,14], training of artificial neural networks [15,16], and many others.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Particle Swarm Optimization Algorithm via Adaptive Dynamic Inertia Weight and Acceleration Coefficients

Sekyere,

Effah,

Okyere

2024

J. Electron. Electric. Eng.

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The particle swarm optimization (PSO) algorithm counts among the most popular metaheuristic algorithms based on swarm intelligence. Since the publication of the first article on this optimization technique, researchers have developed many PSO variants with some improvement in its performance. The PSO optimization performance hinges on its ability to achieve a good exploration-exploitation balance. The most common method that helps to improve exploration-exploitation balance is modifying the PSO three controlling parameters, namely the inertia weight and two acceleration coefficients. In this paper a PSO variant that combines adaptive dynamic inertia weight and adaptive dynamic acceleration coefficients to enhance the exploration-exploitation balance of the PSO is proposed. The enhanced PSO algorithm called Adaptive Dynamic Inertia Weight and Acceleration Coefficient Optimization (ADIWACO) algorithm is tested on seven well-known standard test functions comprising four unimodal and three multimodal ones. The performance of the PSO is then compared with that of the standard PSO (SPSO) and four existing PSO variants. The experimental results comprising optimum value, runtime, mean value, standard deviation and convergence rate, and confirmed by the results of ranking statistics and Wilcoxon signed rank test conducted on the experimental results, indicate significantly better performance by the proposed PSO algorithm.

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Acceleration of Particle Swarm Optimization with AVX Instructions

Cited by 2 publications

References 28 publications

Hydrodynamic Characteristic Analysis and NSGA-II Optimization of a Vacuum Fish Pump

Hydrodynamic Characteristic Analysis and NSGA-II Optimization of a Vacuum Fish Pump

An Enhanced Particle Swarm Optimization Algorithm via Adaptive Dynamic Inertia Weight and Acceleration Coefficients

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