Particle swarm ... the optimization, including the beam span (15 m), the permanent distributed load (20 kN/m), and the variable distributed load (10 kN/m). 2.2 Structural constraints Considering all ...

Specifically, the Multi-objective Particle Swarm Optimization (MOPSO) algorithm, implemented within the MATLAB environment ... the time-of-day tariff structure employed in this study is meticulously ...

This paper aims to implement PSO algorithm in Matlab code in order to determine the ... models with a good agreement with the Rivlin’s data for the torsionnal couple curve; 2) The particle swarm ...

In section Empirical Analyses, the stock prediction model developed in this paper is measured by applying the stock data of two leading ... proposed a particle swarm optimization with quantum behavior ...

methods face the challenge of “curse of dimensionality” when dealing with high-dimensional data. Focusing on this challenge, this article studies a variable-size cooperative coevolutionary particle ...

Evolutionary Feature Selection for Classification We are more than happy to share the related Matlab ... adaptive particle swarm optimization for large-scale feature selection in classification, ACM ...

Abstract: With a global search mechanism, particle swarm optimization ... on data with much higher dimensionality which has become more and more popular with the advance of data collection ...

Although mathematically elegant, back-propagation isn't perfect. Instead consider using particle swarm optimization (PSO) to train your neural network; here's how. You can think of a neural network as ...