Abstract
A random signal-based learning merged with simulated annealing (SARSL), which is serial algorithm, has been considered by the authors. But the serial nature of SARSL degrades its performance as the complexity of the search space is increasing. To solve this problem, this paper proposes a population structure of SARSL (PSARSL) which enables multi-point search. Moreover, adaptive partitioning method (APM) is used to reduce the optimization time. The validity of the proposed algorithm is conformed by applying it to a complex test function example and a general version of fuzzy controller design.
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© 2006 Springer-Verlag Berlin Heidelberg
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Han, CW., Park, JI. (2006). Population Structure of Heuristic Search Algorithm Based on Adaptive Partitioning. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_27
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DOI: https://doi.org/10.1007/11779568_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35453-6
Online ISBN: 978-3-540-35454-3
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