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The Particle Swarm Optimizer, like many other evolutionary and classical minimization methods, su ers the problem of occasional convergence to local minima, especially in multimodal and scattered landscapes. In this work we propose a modi cation of the Particle Swarm Optimizer that makes use of a new technique, named Function\Stretching", to alleviate the local minima problem. Function\Stretching" consists of a two {stage transformation of the objective function that eliminates local minima, while preserving global ones. Experiments indicate that the Particle Swarm Optimizer equipped with the\Stretching" technique exhibits good performance and results in nding global minima reliably and predictably.