Abstract
This paper investigates a global search optimisation technique, referred to as the repeated weighted boosting search. The proposed optimisation algorithm is extremely simple and easy to implement. Heuristic explanation is given for the global search capability of this technique. Comparison is made with the two better known and widely used global search techniques, known as the genetic algorithm and adaptive simulated annealing. The effectiveness of the proposed algorithm as a global optimiser is investigated through several examples.
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© 2005 Springer-Verlag Berlin Heidelberg
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Chen, S., Wang, X.X., Harris, C.J. (2005). A Search Algorithm for Global Optimisation. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_152
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DOI: https://doi.org/10.1007/11539117_152
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
Online ISBN: 978-3-540-31858-3
eBook Packages: Computer ScienceComputer Science (R0)Springer Nature Proceedings Computer Science
Keywords
- Mean Square Error
- Global Search
- Channel Impulse Response
- Normalise Mean Square Error
- Convergence Performance
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
