This paper proposes an enhanced ant colony optimization with dynamic mutation and ad hoc initialization, ACODM-I, for\nimproving the accuracy of Takagi-Sugeno-Kang- (TSK-) type fuzzy systems design. Instead of the generic initialization usually\nused in most population-based algorithms, ACODM-I proposes an ad hoc application-specific initialization for generating the\ninitial ant solutions to improve the accuracy of fuzzy system design. The generated initial ant solutions are iteratively improved\nby a new approach incorporating the dynamic mutation into the existing continuous ACO (ACOR). The introduced dynamic\nmutation balances the exploration ability and convergence rate by providing more diverse search directions in the early stage of\noptimization process. Application examples of two zero-order TSK-type fuzzy systems for dynamic plant tracking control and\none first-order TSK-type fuzzy system for the prediction of the chaotic time series have been simulated to validate the proposed\nalgorithm. Performance comparisons with ACOR and different advanced algorithms or neural-fuzzy models verify the superiority\nof the proposed algorithm. The effects on the design accuracy and convergence rate yielded by the proposed initialization and\nintroduced dynamic mutation have also been discussed and verified in the simulations.
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