Abstract
This paper proposes a new population-based evolutionary optimization algorithm, elite-mixed continuous ant colony optimization with central initialization (EMCACO-C), for improving the accuracy of Takagi–Sugeno–Kang-type recurrent fuzzy network (TRFN) designs. The EMCACO-C is a stochastic search algorithm. The EMCACO-C initializes the ant solutions on concentrative region around the center of the search range followed by a new designed elite-mixed continuous ant colony optimization to generate new solutions. The EMCACO-C mixes the few best elites to generate the directional solutions for guiding and exploring possible promising regions. Then the EMCACO-C employs the Gaussian random sampling to exploit further the directional solutions for finding better solutions. The methodology similarities and differences between the EMCACO-C and genetic algorithm are analyzed. The performances of the EMCACO-C for TRFN designs are verified in the simulations of five application examples including dynamic system control, dynamic system identification, and chaotic series prediction. The EMCACO-C performance is also compared with other swarm-based evolutionary algorithms in the simulations.
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