Optimization and learning of load restoration strategies
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This paper presents a genetic algorithm to seek the optimal location of multi-type FACTS devices in a power system. The optimizations are performed on three parameters: the location of the devices, their types and their values. The system loadability is ap ...
Synthetic yet realistic images are valuable for many applications in visual sciences and medical imaging. Typically, investigators develop algorithms and adjust their parameters to generate images that are visually similar to real images. In this study, we ...
We propose an online learning algorithm to tackle the problem of learning under limited computational resources in a teacher-student scenario, over multiple visual cues. For each separate cue, we train an online learning algorithm that sacrifices performan ...
System-level design decisions such as HW/SW partitioning, target architecture selection and scheduler selection are some of the main concerns of current complex system-on-chip (SOC) designs. In this paper, a novel window-based heuristic is proposed that ad ...
This report presents a new methodological approach for the optimal design of energy integrated batch processes. The main emphasis is put on indirect and, to some extend, on direct heat exchange networks with the possibility of introducing closed or open st ...
In the last few years several researchers have resorted to artificial evolution (e.g. genetic algorithms) and learning techniques (e.g. neural networks) for studying the interaction between learning and evolution. These studies have been conducted for two ...
An efficient hybrid method to optimize the phase states distribution, or phase diagram, of a digitally-reconfigurable reflective cell is presented. It allows minimizing phase quantization errors in applications such as reflect arrays. Digital control of th ...
We explore using particle swarm optimization on problems with noisy performance evaluation, focusing on unsupervised robotic learning. We adapt a technique of overcoming noise used in genetic algorithms for use with particle swarm optimization, and evaluat ...
This paper presents a new double hybridized genetic algorithm for optimizing the variable order in Reduced Ordered Binary Decision Diagrams. The first hybridization adopts embryonic chromosomes as prefixes of variable orders instead of complete variable or ...
This paper presents a method to optimize two linear actuator configurations. The method is stochastic and combines a genetic algorithm (GA) and FEM (finite element method) model generated with the commercial software FEMM. The optimization is performed in ...