Search results for "Swarm"
showing 10 items of 100 documents
Applying particle swarm optimization to the motion-cueing-algorithm tuning problem
2017
The MCA tuning problem consists in finding the best values for the parameters/coefficients of Motion Cueing Algorithms (MCA). MCA are used to control the movements of robotic motion platforms employed to generate inertial cues in vehicle simulators. This problem is traditionally approached with a manual pilot-in-the-loop subjective tuning, based on the opinion of several pilots/drivers. Instead, this paper proposes applying Particle Swarm Optimization (PSO) to solve this problem, using simulated motion platforms and objective indicators rather than subjective opinions. Results show that PSO-based tuning can provide a suitable solution for this complex optimization problem.
Binding mode analysis of ABCA7 for the prediction of novel Alzheimer's disease therapeutics
2021
Graphical abstract
Computational Analysis of the Active Control of Incompressible Airfoil Flutter Vibration Using a Piezoelectric V-Stack Actuator
2021
The flutter phenomenon is a potentially destructive aeroelastic vibration studied for the design of aircraft structures as it limits the flight envelope of the aircraft. The aim of this work is to propose a heuristic design of a piezoelectric actuator-based controller for flutter vibration suppression in order to extend the allowable speed range of the structure. Based on the numerical model of a three degrees of freedom (3DOF) airfoil and taking into account the FEM model of a V-stack piezoelectric actuator, a filtered PID controller is tuned using the population decline swarm optimizer PDSO algorithm, and gain scheduling (GS) of the controller parameters is used to make the control adapti…
Hybrid Particle Swarm Optimization With Genetic Algorithm to Train Artificial Neural Networks for Short-Term Load Forecasting
2019
This research proposes a new training algorithm for artificial neural networks (ANNs) to improve the short-term load forecasting (STLF) performance. The proposed algorithm overcomes the so-called training issue in ANNs, where it traps in local minima, by applying genetic algorithm operations in particle swarm optimization when it converges to local minima. The training ability of the hybridized training algorithm is evaluated using load data gathered by Electricity Generating Authority of Thailand. The ANN is trained using the new training algorithm with one-year data to forecast equal 48 periods of each day in 2013. During the testing phase, a mean absolute percentage error (MAPE) is used …
Application of A Precision Apiculture System to Monitor Honey Daily Production †
2020
Precision beekeeping or precision apiculture is an apiary management strategy based on the monitoring of individual bee colonies to minimize resource consumption and maximize the productivity of bees. Bees play a fundamental role in ensuring pollination
Ancillary Services in the Energy Blockchain for Microgrids
2019
The energy blockchain is a distributed Internet protocol for energy transactions between nodes of a power system. Recent applications of the energy blockchain in microgrids only consider the energy transactions between peers without considering the technical issues that can arise, especially when the system is islanded. One contribution of the paper is, thus, to depict a comprehensive framework of the technical and economic management of microgrids in the blockchain era, considering, for the first time, the provision of ancillary services and, in particular, of the voltage regulation service. When more PV nodes are operating in the grid, large reactive power flows may appear in the branches…
Lemon Oils Attenuate the Pathogenicity of Pseudomonas aeruginosa by Quorum Sensing Inhibition
2021
The chemical composition of three Citrus limon oils: lemon essential oil (LEO), lemon terpenes (LT) and lemon essence (LE), and their influence in the virulence factors production and motility (swarming and swimming) of two Pseudomonas aeruginosa strains (ATCC 27853 and a multidrug-resistant HT5) were investigated. The main compound, limonene, was also tested in biological assays. Eighty-four compounds, accounting for a relative peak area of 99.23%, 98.58% and 99.64%, were identified by GC/MS. Limonene (59–60%), γ-terpinene (10–11%) and β-pinene (7–15%) were the main compounds. All lemon oils inhibited specific biofilm production and bacterial metabolic activities into biofilm in a dose-dep…
Impact of chaotic dynamics on the performance of metaheuristic optimization algorithms : An experimental analysis
2022
Random mechanisms including mutations are an internal part of evolutionary algorithms, which are based on the fundamental ideas of Darwin's theory of evolution as well as Mendel's theory of genetic heritage. In this paper, we debate whether pseudo-random processes are needed for evolutionary algorithms or whether deterministic chaos, which is not a random process, can be suitably used instead. Specifically, we compare the performance of 10 evolutionary algorithms driven by chaotic dynamics and pseudo-random number generators using chaotic processes as a comparative study. In this study, the logistic equation is employed for generating periodical sequences of different lengths, which are use…
A numerical study of attraction/repulsion collective behavior models: 3D particle analyses and 1D kinetic simulations
2013
39p; International audience; We study at particle and kinetic level a collective behavior model based on three phenomena: self-propulsion, friction (Rayleigh effect) and an attractive/repulsive (Morse) potential rescaled so that the total mass of the system remains constant independently of the number of particles N . In the first part of the paper, we introduce the particle model: the agents are numbered and described by their position and velocity. We iden- tify five parameters that govern the possible asymptotic states for this system (clumps, spheres, dispersion, mills, rigid-body rotation, flocks) and perform a numerical analysis on the 3D setting. Then, in the second part of the paper…
Modelling swarm-intelligent systems for medical applications
2017
Modeling swarm intelligent systems has attracted attention of researchers over the last decade, as the attributes such as self-organization, self-regulation or collective behavior exhibited by the system entities while following a certain set of rules, can be implemented with the aim at investigating complexity of the problems that an individual would be unable to tackle in real world. In this keynote paper, meta-heuristics and paradigms of modeling swarm-intelligent systems will be discussed with respect to their application areas for medical purposes.