Abstract
This paper presents a new bio-inspired algorithm named Competitive Quantum-Behaviour Evolutionary Multi-Swarm Optimization (CQEMSO) based on CUDA parallel architecture applied to solve engineering problems, using the concept of master/slave swarm working under a competitive scheme and being executed over the paradigm of General Purpose Computing on Graphics Processing Units (GPGPU). The efforts on implementing the CQEMSO algorithm are focused at generating a solution which includes greater quality of search and higher speed of convergence by using mechanisms of evolutionary strategies with the procedures of search and optimization found in the classic QPSO. For performance analysis, the proposed solution was submitted to some well-known engineering problems (WBD, DPV) and its results compared to other solutions found on scientific literature.
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Leal Souza, D., Noura Teixeira, O., Cavalcante Monteiro, D., Célio Limão de Oliveira, R., Antônio Florenzano Mollinetti, M. (2014). A Novel Competitive Quantum-Behaviour Evolutionary Multi-Swarm Optimizer Algorithm Based on CUDA Architecture Applied to Constrained Engineering Design. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2014. Lecture Notes in Computer Science, vol 8667. Springer, Cham. https://doi.org/10.1007/978-3-319-09952-1_18
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DOI: https://doi.org/10.1007/978-3-319-09952-1_18
Publisher Name: Springer, Cham
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