Abstract
Simulated binary crossover (SBX) operator is widely used in real-coded genetic algorithms. Particle swarm optimization (PSO) is a well-studied optimization scheme. In this paper, we combine SBX together with particle swarm optimization (PSO) procedures to prevent possible premature convergence. Benchmark tests are implemented and the result turns out that such modification enhances the exploitation ability of PSO.
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Huang, X., Lin, E., Ji, Y., Qiao, S. (2011). Using Simulated Binary Crossover in Particle Swarm Optimization. In: Wang, Y., Li, T. (eds) Knowledge Engineering and Management. Advances in Intelligent and Soft Computing, vol 123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25661-5_12
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DOI: https://doi.org/10.1007/978-3-642-25661-5_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25660-8
Online ISBN: 978-3-642-25661-5
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