Empirical Study of Artificial Fish Swarm Algorithm
  Artificial fish swarm algorithm (AFSA) is one of the swarm intelligence
optimization algorithms that works based on population and stochastic search.
In order to achieve acceptable result, there are many parameters needs to be
adjusted in AFSA. Among these parameters, visual and step are very significant
in view of the fact that artificial fish basically move based on these
parameters. In standard AFSA, these two parameters remain constant until the
algorithm termination. Large values of these parameters increase the capability
of algorithm in global search, while small values improve the local search
ability of the algorithm. In this paper, we empirically study the performance
of the AFSA and different approaches to balance between local and global
exploration have been tested based on the adaptive modification of visual and
step during algorithm execution. The proposed approaches have been evaluated
based on the four well-known benchmark functions. Experimental results show
considerable positive impact on the performance of AFSA.
