The Artificial Fish Swarm Algorithm Optimized by RNA Computing

In the initial period, the peculiarity of artificial fish swarm algorithm is of fast searching speed and high optimization accuracy, but in the later period, the convergence speed is always slow, and artificial fish tend to gather around the local optimum. Therefore, the solving ability of the algorithm becomes weak and the global optimal value is hard to obtain. Considering the introduction of RNA computation based on biomolecular operations, the optimization capability of traditional algorithm can be enhanced effectively. Therefore, RNA computing is introduced to artificial fish swarm algorithm, and a modified artificial fish swarm algorithm is presented on the grounds of RNA computing. In the later period of artificial fish swarm algorithm, the transformation, replacement and recombination operations in RNA computation are applied to increase diversity of artificial fish, so as to further the convergence speed and optimization capability of the algorithm. In the meantime, the improved algorithm, RNA-AFSA, is tested by four typical functions, and the results prove that the modified artificial fish swarm algorithm has better optimization effects in search accuracy, stability, and other aspects.