Abstract
In this paper, we describe an imperialist competitive algorithm with dynamic adjustment of parameters using fuzzy logic to adjust the Beta and Xi parameters. We are considering different fuzzy systems to measure the performance of the algorithm with six benchmark mathematical functions with different number of decades and performing 30 experiments for each case. The results demonstrate the efficiency of the fuzzy ICA algorithm in optimization problems and give us the guidelines for future work.
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Acknowledgment
We would like to express our gratitude to the CONACYT and Tijuana Institute of Technology for the facilities and resources granted for the development of this research.
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Bernal, E., Castillo, O., Soria, J. (2017). Imperialist Competitive Algorithm with Dynamic Parameter Adaptation Applied to the Optimization of Mathematical Functions. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Nature-Inspired Design of Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 667. Springer, Cham. https://doi.org/10.1007/978-3-319-47054-2_22
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DOI: https://doi.org/10.1007/978-3-319-47054-2_22
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