Comparative learning global particle swarm optimization for optimal distributed generations' output

Comparative learning global particle swarm optimization for optimal distributed generations’ output

The appropriate output of distributed generation (DG) in a distribution network is important for maximizing the benefit of the DG installation in the network. Therefore, most researchers have concentrated on the optimization technique to compute the optimal DG value. In this paper, the comparative learning in global particle swarm optimization (CLGPSO) method is introduced. The implementation o...

Optimal multiple distributed generation output through rank evolutionary particle swarm optimization

Distributed vs. Centralized Particle Swarm Optimization for Learning Flocking Behaviors

In this paper we address the automatic synthesis of controllers for the coordinated movement of multiple mobile robots. We use a noise-resistant version of Particle Swarm Optimization to learn in simulation a set of 50 weights of a plastic artificial neural network. Two learning strategies are applied: homogeneous centralized learning, in which every robot runs the same controller and the perfo...

Optimal Bandwidth Design for Lazy Learning Via Particle Swarm Optimization

Lazy learning is a memory-based learning techniques, which performs local regression around a point of interest. Because of simplicity and ease in application, it has been successfully used in a large number of complex systems. However, a crucial step in this algorithm is how to design the bandwidth h, which controls the precision of the prediction. Unlike conventional trial and error tricks, t...

Distributed Multi-Robot Learning using Particle Swarm Optimization

This thesis studies the automatic design and optimization of high-performing robust controllers for mobile robots using exclusively on-board resources. Due to the often large parameter space and noisy performance metrics, this constitutes an expensive optimization problem. Population-based learning techniques have been proven to be effective in dealing with noise and are thus promising tools to...