Abstract
Particle Swarm Optimization (PSO) is a kind of classical population-based intelligent optimization methods that widely used in solving various optimization problems. With the increase of the dimensions of the optimized problem, the high-dimensional particle swarm optimization becomes an urgent, practical and popular issue. Based on data similarly measurement, a high-dimensional PSO algorithm is proposed to solve the high-dimensional problems. The study primarily defines a new distance paradigm based on the existing similarity measurement of high-dimensional data. This is followed by proposes a PSO variant under the new distance paradigm, namely the LPSO algorithm, which is extended from the classical Euclidean space to the metric space. Finally, it is showed that LPSO could obtain better solution at higher convergence speed in high-dimensional search space.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant 61401283, in part by the Educational Commission of Guangdong Province, China under Grant 2014KTSCX113, and in part by the Fundamental Research Funds for the Central Universities under Grant GK201703062 and GK201603014.
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Feng, J., Lai, G., Cheng, S., Zhang, F., Sun, Y. (2017). A High-Dimensional Particle Swarm Optimization Based on Similarity Measurement. In: Tan, Y., Takagi, H., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10385. Springer, Cham. https://doi.org/10.1007/978-3-319-61824-1_20
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DOI: https://doi.org/10.1007/978-3-319-61824-1_20
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