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In this paper we study the performance of the recently proposed Particle Swarm optimization method in the presence of noisy and continuously changing environments. Experimental results for well known and widely used optimization test functions are given and discussed. Conclusions for its ability to cope with such environments as well as real–life applications are also derived.