  Artificial Bee Colony (ABC) optimization algorithm is one of the recent
population based probabilistic approach developed for global optimization. ABC
is simple and has been showed significant improvement over other Nature
Inspired Algorithms (NIAs) when tested over some standard benchmark functions
and for some complex real world optimization problems. Memetic Algorithms also
become one of the key methodologies to solve the very large and complex
real-world optimization problems. The solution search equation of Memetic ABC
is based on Golden Section Search and an arbitrary value which tries to balance
exploration and exploitation of search space. But still there are some chances
to skip the exact solution due to its step size. In order to balance between
diversification and intensification capability of the Memetic ABC, it is
randomized the step size in Memetic ABC. The proposed algorithm is named as
Randomized Memetic ABC (RMABC). In RMABC, new solutions are generated nearby
the best so far solution and it helps to increase the exploitation capability
of Memetic ABC. The experiments on some test problems of different complexities
and one well known engineering optimization application show that the proposed
algorithm outperforms over Memetic ABC (MeABC) and some other variant of ABC
algorithm(like Gbest guided ABC (GABC),Hooke Jeeves ABC (HJABC), Best-So-Far
ABC (BSFABC) and Modified ABC (MABC) in case of almost all the problems.
