Abstract
A new hybrid Particle Swarm Optimization (PSO) algorithm is proposed based on the Nonlinear Simplex Search (NSS) method. At late stage of PSO, when the most promising regions of solutions are fixed, the algorithm isolates particles that are very close to the extrema, and applies the NSS method to them to enhance local exploitation searching. Explicit experimental results on famous benchmark functions indicate that this approach is reliable and efficient, especially on multi-model function optimizations. It yields better solution qualities and success rates compared to other published methods.
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© 2005 Springer-Verlag Berlin Heidelberg
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Wang, F., Qiu, Y., Feng, N. (2005). Multi-model Function Optimization by a New Hybrid Nonlinear Simplex Search and Particle Swarm Algorithm. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_68
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DOI: https://doi.org/10.1007/11539902_68
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28320-1
Online ISBN: 978-3-540-31863-7
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