  Image pattern recognition is an important area in digital image processing.
An efficient pattern recognition algorithm should be able to provide correct
recognition at a reduced computational time. Off late amongst the machine
learning pattern recognition algorithms, Artificial fish swarm algorithm 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. A Convex optimization has been integrated into the
proposed work to have an ideal segmentation of the input image which is a MR
brain image.
