no code implementations • 22 Aug 2022 • Jun Jet Tai, Jordan K. Terry, Mauro S. Innocente, James Brusey, Nadjim Horri
In the case of using an oracle policy, it can be unclear how best to incorporate the oracle policy's experience into the learning policy in a way that maximizes learning sample efficiency.
no code implementations • 28 Jan 2021 • Mauro S. Innocente, Johann Sienz
In its canonical version, there are three factors that govern a particle's trajectory: 1) inertia from its previous displacement; 2) attraction to its best experience; and 3) attraction to a given neighbour's best experience.
no code implementations • 25 Jan 2021 • Mauro S. Innocente, Johann Sienz
The coefficients settings govern the trajectories of the particles towards the good locations identified, whereas the neighbourhood topology controls the form and speed of spread of information within the population (i. e. the update of the social attractor).
no code implementations • 25 Jan 2021 • Mauro S. Innocente, Johann Sienz
Population-based methods can cope with a variety of different problems, including problems of remarkably higher complexity than those traditional methods can handle.
no code implementations • 25 Jan 2021 • Carwyn Pelley, Mauro S. Innocente, Johann Sienz
The combining of a General-Purpose Particle Swarm Optimizer (GP-PSO) with Sequential Quadratic Programming (SQP) algorithm for constrained optimization problems has been shown to be highly beneficial to the refinement, and in some cases, the success of finding a global optimum solution.
no code implementations • 25 Jan 2021 • Mauro S. Innocente, Johann Sienz
While the particle swarm optimizers share such advantages, their main desirable features when compared to evolutionary algorithms are their lower computational cost and easier implementation, involving no operator design and few parameters to be tuned.
no code implementations • 25 Jan 2021 • Mauro S. Innocente
In addition, some constraint-handling techniques are incorporated into the canonical algorithm to handle inequality constraints.
no code implementations • 25 Jan 2021 • Mauro S. Innocente, Johann Sienz
The penalization method is a popular technique to provide particle swarm optimizers with the ability to handle constraints.
no code implementations • 25 Jan 2021 • Johann Sienz, Mauro S. Innocente
The importance awarded to each factor is controlled by three coefficients: the inertia; the individuality; and the sociality weights.
no code implementations • 25 Jan 2021 • Johann Sienz, Mauro S. Innocente
The advantages of evolutionary algorithms with respect to traditional methods have been greatly discussed in the literature.
no code implementations • 25 Jan 2021 • Mauro S. Innocente, Johann Sienz
Traditional methods present a very restrictive range of applications, mainly limited by the features of the function to be optimized and of the constraint functions.
1 code implementation • 14 Dec 2020 • Jun Jet Tai, Mauro S. Innocente, Owais Mehmood
In this work, a novel high-speed railway fastener detector is introduced.