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• W2111980587 abstract "A hybrid Particle Swarm Optimization (PSO) that features an automatic termination and better search efficiency than classical PSO is presented. The proposed method is combined with the so-called Gene Matrix to provide the search with a self-check in order to determine a proper termination instant. Its convergence speed and reliability are also increased by the implementation of the Principal Component Analysis (PCA) technique and the hybridization with a local search method. The proposed algorithm is denominated as Automatically Terminated Particle Swarm Optimization with Principal Component Analysis (AT-PSO-PCA). The computational experiments demonstrate the effectiveness of the automatic termination criteria and show that AT-PSO-PCA enhances the convergence speed, accuracy and reliability of the PSO paradigm. Furthermore, comparisons with state-of-the-art evolutionary algorithms (EA) yield competitive results even under the automatically detected termination instant." @default.
• W2111980587 created "2016-06-24" @default.
• W2111980587 creator A5004254506 @default.
• W2111980587 creator A5058813836 @default.
• W2111980587 date "2015-01-01" @default.
• W2111980587 modified "2023-09-27" @default.
• W2111980587 title "Automatically Terminated Particle Swarm Optimization with Principal Component Analysis" @default.
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• W2111980587 doi "https://doi.org/10.1142/s0219622014500837" @default.
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