11 citations · 40 across the 15 of their papers we have counts for
17 papers
PyFlyt -- UAV Simulation Environments for Reinforcement Learning Research
Jun Jet Tai, Jim Wong, Mauro Innocente +3
Unmanned aerial vehicles (UAVs) have numerous applications, but their efficient and optimal flight can be a challenge. Reinforcement Learning (RL) has emerged as a promising approa…
Some Supervision Required: Incorporating Oracle Policies in Reinforcement Learning via Epistemic Uncertainty Metrics
Jun Jet Tai, Jordan K. Terry, Mauro S. Innocente +2
An inherent problem of reinforcement learning is performing exploration of an environment through random actions, of which a large portion can be unproductive. Instead, exploration…
Synthesis and Characterisation of Iron Oxide Nanoparticles with Tunable Sizes by Hydrothermal Method
Ioannis Papagiannis, Mauro S. Innocente, Evangelos I. Gkanas
The present study investigates the effect of different reaction times on the crystallinity, surface morphology and size of iron oxide nanoparticles. In this synthetic system, aqueo…
Stigmergy-based collision-avoidance algorithm for self-organising swarms
Paolo Grasso, Mauro Sebastián Innocente
Real-time multi-agent collision-avoidance algorithms comprise a key enabling technology for the practical use of self-organising swarms of drones. This paper proposes a decentralis…
Particle Swarms Reformulated towards a Unified and Flexible Framework
Mauro Sebastián Innocente
The Particle Swarm Optimisation (PSO) algorithm has undergone countless modifications and adaptations since its original formulation in 1995. Some of these have become mainstream w…
Coefficients' Settings in Particle Swarm Optimization: Insight and Guidelines
Mauro S. Innocente, Johann Sienz
Particle Swam Optimization is a population-based and gradient-free optimization method developed by mimicking social behaviour observed in nature. Its ability to optimize is not sp…
Pseudo-Adaptive Penalization to Handle Constraints in Particle Swarm Optimizers
Mauro S. Innocente, Johann Sienz
The penalization method is a popular technique to provide particle swarm optimizers with the ability to handle constraints. The downside is the need of penalization coefficients wh…
Individual and Social Behaviour in Particle Swarm Optimizers
Johann Sienz, Mauro S. Innocente
Three basic factors govern the individual behaviour of a particle: the inertia from its previous displacement; the attraction to its own best experience; and the attraction to a gi…
Particle Swarm Optimization: Fundamental Study and its Application to Optimization and to Jetty Scheduling Problems
Johann Sienz, Mauro S. Innocente
The advantages of evolutionary algorithms with respect to traditional methods have been greatly discussed in the literature. While particle swarm optimizers share such advantages,…
Combining Particle Swarm Optimizer with SQP Local Search for Constrained Optimization Problems
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 b…
Numerical Comparison of Neighbourhood Topologies in Particle Swarm Optimization
Mauro S. Innocente, Johann Sienz
Particle Swarm Optimization is a global optimizer in the sense that it has the ability to escape poor local optima. However, if the spread of information within the population is n…
Constraint-Handling Techniques for Particle Swarm Optimization Algorithms
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. The main proce…
Population-Based Methods: PARTICLE SWARM OPTIMIZATION -- Development of a General-Purpose Optimizer and Applications
Mauro S. Innocente
This thesis is concerned with continuous, static, and single-objective optimization problems subject to inequality constraints. Nevertheless, some methods to handle other kinds of…
A Study of the Fundamental Parameters of Particle Swarm Optimizers
Mauro S. Innocente, Johann Sienz
The range of applications of traditional optimization methods are limited by the features of the object variables, and of both the objective and the constraint functions. In contra…
Optimal Flexural Design of FRP-Reinforced Concrete Beams Using a Particle Swarm Optimizer
M. S. Innocente, Ll. Torres, X. Cahís +2
The design of the cross-section of an FRP-reinforced concrete beam is an iterative process of estimating both its dimensions and the reinforcement ratio, followed by the check of t…
Particle Swarm Optimization: Development of a General-Purpose Optimizer
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. In contrast, e…
FasteNet: A Fast Railway Fastener Detector
Jun Jet Tai, Mauro S. Innocente, Owais Mehmood
In this work, a novel high-speed railway fastener detector is introduced. This fully convolutional network, dubbed FasteNet, foregoes the notion of bounding boxes and performs dete…