Particle Swarm Optimization (PSO) is an evolutionary heuristics-based method used for continuous function optimization. Compared to 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 number and characteristics of the exploited processors. With the advent and large availability of Graphics Processing Units (GPUs) and the development and straightforward applicability of the Compute Unified Device Architecture platform (CUDA), several applications have benefited from the reduction of the execution time, exploiting massive parallelism. In this paper, we propose an alternative algorithm to massively parallelize the PSO algorithm and mapped it onto a GPU-based architecture. The algorithm focuses on the work done with respect to each of the problem dimension and does it in parallel.
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