An Improved Artificial Bee Colony Algorithm for Staged Search
Abstract: Artificial Bee
Colony(ABC) or its improved algorithms used in solving high dimensional complex
function optimization issues has some disadvantages, such as lower convergence,
lower solution precision, lots of control parameters of improved algorithms,
easy to fall into a local optimum solution. In this letter, we propose an
improved ABC of staged search. This new algorithm designs staged employed bee
search strategy which makes that employed bee has different search characters
in different stages. That reduces probability of falling into local extreme
value. It defines the escape radius which can guide precocious individual to
jump local extreme value and avoid the blindness of flight behavior. Meanwhile,
we adopt initialization strategy combining uniform distribution and backward
learning to prompt initial solution with uniform distribution and better
quality. Finally, we make simulation experiments for eight typical high
dimensional complex functions. Results show that the improved algorithm has a
highersolution precision and faster convergence rate which is more suitable for
solving high dimensional complex functions.
Keywords: artificial bee
colony, staged search, function optimization, escape radius, uniform
distribution,backward learning
Author: Shoulin Yin, Jie Liu,
Lin Teng
Journal Code: jptkomputergg160311
