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• W2032743747 abstract "In order to escape from premature convergence and improve the efficiency of the Quantum-behaved particle swarm optimization (QPSO) algorithm, this paper propose a new algorithm PDCQPSO, which employing diversity-controlled mechanism into QPSO to increase the diversity of population and parallel technique to shorten the running time of algorithm. A comprehensive experimental study is conducted on a set of benchmark functions, Comparison results show that PDCQPSO obtains a promising performance and less time cost on the majority of the test problems." @default.
• W2032743747 created "2016-06-24" @default.
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• W2032743747 date "2014-11-01" @default.
• W2032743747 modified "2023-10-18" @default.
• W2032743747 title "Parallel Diversity-Controlled Quantum-Behaved Particle Swarm Optimization Algorithm" @default.
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• W2032743747 doi "https://doi.org/10.1109/cis.2014.53" @default.
• W2032743747 hasPublicationYear "2014" @default.
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