Abstract
Particle Swarm Optimization (PSO) is a stochastic metaheuristics yet very robust. Real-world optimizations require a high computational effort to converge to a viable solution. In general, parallel PSO implementations provide good performance, but this depends on the parallelization strategy as well as the number and/or characteristics of the exploited processors. In this paper, we propose a fine-grained paralellization strategy that focuses on the work done w.r.t. each of the problem dimensions and does it in parallel. Moreover, all particles act in parallel. This strategy is useful in computationally demanding optimization problems wherein the objective function has a very large number of dimensions. We map the computation onto three different parallel high-performance multiprocessor architectures, which are based on many and multi-core architectures. The performance of the proposed strategy is evaluated for four well-known benchmarks with high-dimension and different complexity. The obtained speedups are very promising.
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Nedjah, N., de Moraes Calazan, R., de Macedo Mourelle, L. (2017). A Fine-Grained Parallel Particle Swarm Optimization on Many-core and Multi-core Architectures. In: Malyshkin, V. (eds) Parallel Computing Technologies. PaCT 2017. Lecture Notes in Computer Science(), vol 10421. Springer, Cham. https://doi.org/10.1007/978-3-319-62932-2_20
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DOI: https://doi.org/10.1007/978-3-319-62932-2_20
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