Abstract
The proposed approach incorporated dynamic guiding approach and chaotic search procedure into particle swarm optimization (PSO), named DCPSO. Chaotic search, enjoyed ergodicity, irregularity and pseudo-randomness in PSO, would refine global best position evidently. And, dynamic guiding approach with fluctuating property would easily conduct unpredictable migrations for PSO to break away from evolutionary stagnation. The experiment reports indicated that the proposed DCPSO approach could improve the evolution performance significantly, and present the superiority in solving complex multidimensional problems.
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Cheng, MY., Huang, KY. & Chen, HM. Dynamic guiding particle swarm optimization with embedded chaotic search for solving multidimensional problems. Optim Lett 6, 719–729 (2012). https://doi.org/10.1007/s11590-011-0297-z
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DOI: https://doi.org/10.1007/s11590-011-0297-z