Abstract
We consider multistage control problem under fuzzy constraints on controls applied and fuzzy goals on states attained, with a stochastic system under control (a Markov chain). We seek an optimal sequence of controls which maximizes the probability of attaining the fuzzy goal subject to the fuzzy constraints, over a finite, fixed and specified planning horizon. We present an extension of Kacprzyk’s [10, 12] approach, based on a traditional genetic algorithm, by employing a bacterial evolutionary algorithm in the setting of Nawa and Furuhashi [18]. We show that it yields an improved efficiency, and potentials for future extensions.
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Kacprzyk, J. (2015). Multistage Fuzzy Control of a Stochastic System Using a Bacterial Genetic Algorithm. In: Grzegorzewski, P., Gagolewski, M., Hryniewicz, O., Gil, M. (eds) Strengthening Links Between Data Analysis and Soft Computing. Advances in Intelligent Systems and Computing, vol 315. Springer, Cham. https://doi.org/10.1007/978-3-319-10765-3_32
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DOI: https://doi.org/10.1007/978-3-319-10765-3_32
Publisher Name: Springer, Cham
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