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Particle swarm optimization (PSO) is an evolutionary heuristics-based method used for continuous function optimization. Compared with existing stochastic methods, PSO is very robust. Nevertheless, for real-world optimizations, it requires a high computational effort. In general, parallel implementations of PSO provide better performance. However, this depends heavily on the parallelization strategy engineered as well as the number and characteristics of the exploited processors. In this paper, we analyze three different parallelization strategies: a Particle-oriented Strategy (PoS); a Dimension-oriented Strategy (DoS) and a Cooperation-oriented Strategy (CoS). PoS parallelizes the particle's work. DoS focuses on the work done with respect to each of the problem dimensions and does it in parallel. CoS subdivides the optimization problem into many simpler subproblems, each of which focuses on a distinct subset of the original problem dimensions. The optimization work for all the yielded subproblems is done in parallel. Note that in the second and third strategies, all particles act in parallel too. We map the three strategies onto a Graphics Processing Units (GPU)-based architecture. The performance of the implementations is evaluated using four benchmark functions, considering high-dimension instances. We compare the speedups achieved by the GPU-based implementations of the considered parallelization strategies to the reference sequential PSO implementation as well as to existing PSO implementation on Graphics Processing Units. [ABSTRACT FROM AUTHOR]