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A new meta-heuristics is introduced here: the Multi-Particle Collision Algorithm (M-PCA). The M-PCA is based on the implementation of a function optimization algorithm driven for a collision process of multiple particles. A parallel version for the M-PCA is also described. The complexity for PCA, M-PCA, and a parallel implementation for the MPCA is(More)
We propose to solve function optimization problems with a modified ant colony system: we allow an artificial ants to deposit pheromone, not only on the edges in its path, but also on edges close to them. Before applying this approach, the optimization problem is first transformed into a graphlike one by discretizing the variable domains. We compare the(More)
In this work is proposed an enhancement for the Particle Swarm Optimization (PSO) technique, introducing the concept of a turbulent atmosphere. The original algorithm mimics the behavior of a bird flock in flight, where each bird represents a candidate solution for the problem and updates its position in the search space taking in consideration the previous(More)
We investigate a subclass of ordered statistical filters for image processing, here called OWA filters. They are based on the OWA family of mean operators from the fuzzy sets literature. In particular, we are interested in the use of these filters to reduce speckle in SAR imagery. Moreover, we study how to use genetic algorithms in order to learn the weight(More)