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Found 12 papers, 1 papers with code
Some Supervision Required: Incorporating Oracle Policies in Reinforcement Learning via Epistemic Uncertainty Metrics
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.
Coefficients' Settings in Particle Swarm Optimization: Insight and Guidelines
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.
Particle Swarm Optimization: Development of a General-Purpose Optimizer
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.
Combining Particle Swarm Optimizer with SQP Local Search for Constrained Optimization Problems
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.
Numerical Comparison of Neighbourhood Topologies in Particle Swarm Optimization
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).
Constraint-Handling Techniques for Particle Swarm Optimization Algorithms
Population-based methods can cope with a variety of different problems, including problems of remarkably higher complexity than those traditional methods can handle.
Population-Based Methods: PARTICLE SWARM OPTIMIZATION -- Development of a General-Purpose Optimizer and Applications
In addition, some constraint-handling techniques are incorporated into the canonical algorithm to handle inequality constraints.
A Study of the Fundamental Parameters of Particle Swarm Optimizers
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.
Pseudo-Adaptive Penalization to Handle Constraints in Particle Swarm Optimizers
The penalization method is a popular technique to provide particle swarm optimizers with the ability to handle constraints.
Individual and Social Behaviour in Particle Swarm Optimizers
The importance awarded to each factor is controlled by three coefficients: the inertia; the individuality; and the sociality weights.
Particle Swarm Optimization: Fundamental Study and its Application to Optimization and to Jetty Scheduling Problems
The advantages of evolutionary algorithms with respect to traditional methods have been greatly discussed in the literature.
FasteNet: A Fast Railway Fastener Detector
In this work, a novel high-speed railway fastener detector is introduced.
