  Artificial Bee Colony (ABC) is a distinguished optimization strategy that can
resolve nonlinear and multifaceted problems. It is comparatively a
straightforward and modern population based probabilistic approach for
comprehensive optimization. In the vein of the other population based
algorithms, ABC is moreover computationally classy due to its slow nature of
search procedure. The solution exploration equation of ABC is extensively
influenced by a arbitrary quantity which helps in exploration at the cost of
exploitation of the better search space. In the solution exploration equation
of ABC due to the outsized step size the chance of skipping the factual
solution is high. Therefore, here this paper improve onlooker bee phase with
help of a local search strategy inspired by memetic algorithm to balance the
diversity and convergence capability of the ABC. The proposed algorithm is
named as Improved Onlooker Bee Phase in ABC (IoABC). It is tested over 12 well
known un-biased test problems of diverse complexities and two engineering
optimization problems; results show that the anticipated algorithm go one
better than the basic ABC and its recent deviations in a good number of the
experiments.
