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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)
—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(More)
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