Abstract
In this paper, we introduce a hybrid technique based on particle swarm optimization (PSO) algorithm combined with the nonlinear simplex search method. This approach is applied to multimodal function optimizing tasks. To evaluate its reliability and efficiency, we empirically compare the performance of two variants of the Particle Swarm Optimizer with our hybrid algorithm. The computational results obtained in experiments on large variety of test functions indicate that the hybrid algorithm is competitive with other techniques, and can be successfully applied to more demanding problem domains.
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Wang, F., Qiu, Y., Bai, Y. (2005). A New Hybrid NM Method and Particle Swarm Algorithm for Multimodal Function Optimization. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds) Advances in Intelligent Data Analysis VI. IDA 2005. Lecture Notes in Computer Science, vol 3646. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552253_45
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DOI: https://doi.org/10.1007/11552253_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28795-7
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