Abstract
This paper presents a novel fuzzy modeling strategy based on the hybrid of particle swarm optimization (PSO) and differential evolution (DE), and the proposed hybrid algorithm is referred to as PSODE. PSODE is based on a two-population scheme, in which the individuals in one population is enhanced by PSO and the individuals in the other population is evolved by DE. The individuals both in PSO and DE are co-evolved during the algorithm execution by employing an information sharing mechanisms. To further improve the proposed PSODE algorithm a nonlinear inertia weight approach and a mutation mechanism are presented respectively. In the simulation part, the PSODE is used to automatic design of fuzzy identifier for a nonlinear dynamic system. The performance of the suggested method is compared to PSO, DE and some other methods in the fuzzy identifier design to demonstrate its superiority.
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Niu, B., Li, L. (2008). Design of T-S Fuzzy Model Based on PSODE Algorithm. In: Huang, DS., Wunsch, D.C., Levine, D.S., Jo, KH. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2008. Lecture Notes in Computer Science(), vol 5227. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85984-0_47
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DOI: https://doi.org/10.1007/978-3-540-85984-0_47
Publisher Name: Springer, Berlin, Heidelberg
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