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- W2904277172 abstract "Comprehensive learning particle swarm optimization (CLPSO) enhanced its exploration by comprehensively learning from elite neighbor particles. To further improve the performance of CLPSO, this paper employs chaotic map and nonlinear transformation to generate comprehensive learning probability dynamically, the resultant algorithm is referred to as chaotic comprehensive learning particle swarm optimization with nonlinear transformation (CCLPSO-NT). To evaluate the performance of CCLPSO-NT, a comparison test on the latest CEC 2017 test suite is conducted. The test results show that the proposed CCLPSO-NT outperforms seven representative peer PSO algorithms." @default.
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- W2904277172 date "2018-07-01" @default.
- W2904277172 modified "2023-09-23" @default.
- W2904277172 title "Chaotic comprehensive learning particle swarm optimization with nonlinear transformation" @default.
- W2904277172 doi "https://doi.org/10.1109/ickii.2018.8569153" @default.
- W2904277172 hasPublicationYear "2018" @default.
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