Abstract
Particle swarm optimization (PSO) algorithm can be viewed as a series of iterative matrix computation and its population diversity can be considered as an observation of the distribution of matrix elements. In this paper, PSO algorithm is first represented in the matrix format, then the PSO normalized population diversities are defined and discussed based on matrix analysis. Based on the analysis of the relationship between pairs of vectors in PSO solution matrix, different population diversities are defined for separable and non-separable problems, respectively. Experiments on benchmark functions are conducted and simulation results illustrate the effectiveness and usefulness of the proposed normalized population diversities.
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The authors’ work was supported by National Natural Science Foundation of China under grant No. 60975080, and Suzhou Science and Technology Project under Grant No. SYJG0919.
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Cheng, S., Shi, Y. (2011). Normalized Population Diversity in Particle Swarm Optimization. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21515-5_5
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DOI: https://doi.org/10.1007/978-3-642-21515-5_5
Publisher Name: Springer, Berlin, Heidelberg
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