A Modified Vortex Search Algorithm for Numerical Function Optimization
  The Vortex Search (VS) algorithm is one of the recently proposed
metaheuristic algorithms which was inspired from the vortical flow of the
stirred fluids. Although the VS algorithm is shown to be a good candidate for
the solution of certain optimization problems, it also has some drawbacks. In
the VS algorithm, candidate solutions are generated around the current best
solution by using a Gaussian distribution at each iteration pass. This provides
simplicity to the algorithm but it also leads to some problems along.
Especially, for the functions those have a number of local minimum points, to
select a single point to generate candidate solutions leads the algorithm to
being trapped into a local minimum point. Due to the adaptive step-size
adjustment scheme used in the VS algorithm, the locality of the created
candidate solutions is increased at each iteration pass. Therefore, if the
algorithm cannot escape a local point as quickly as possible, it becomes much
more difficult for the algorithm to escape from that point in the latter
iterations. In this study, a modified Vortex Search algorithm (MVS) is proposed
to overcome above mentioned drawback of the existing VS algorithm. In the MVS
algorithm, the candidate solutions are generated around a number of points at
each iteration pass. Computational results showed that with the help of this
modification the global search ability of the existing VS algorithm is improved
and the MVS algorithm outperformed the existing VS algorithm, PSO2011 and ABC
algorithms for the benchmark numerical function set.
