José Carlos Becceneri

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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)
In this work is proposed an enhancement for<lb>the Particle Swarm Optimization (PSO)<lb>technique, introducing the concept of a<lb>turbulent atmosphere. The original algorithm<lb>mimics the behavior of a bird flock in flight,<lb>where each bird represents a candidate<lb>solution for the problem and updates its<lb>position in the search space taking(More)
Particle Swarm Optimization with Turbulence (PSOT) is, in this paper, applied to find out fuzzy models to represent dynamic behavior of space systems that lie underneath the space qualification process. In optimization area, each minimal improvement in results may represents a maximal, precious meaning and PSOT improve the performance of the established(More)
The Multiple Particle Collision Algorithm (MPCA) is a nature-inspired stochastic optimization method developed specially for high performance computational environments. Its advantages resides in the intense use of computational power provided by multiple processors in the task of search the solution space for a near optimum solution. This work presents the(More)