  Artificial bee colony (ABC) algorithm has proved its importance in solving a
number of problems including engineering optimization problems. ABC algorithm
is one of the most popular and youngest member of the family of population
based nature inspired meta-heuristic swarm intelligence method. ABC has been
proved its superiority over some other Nature Inspired Algorithms (NIA) when
applied for both benchmark functions and real world problems. The performance
of search process of ABC depends on a random value which tries to balance
exploration and exploitation phase. In order to increase the performance it is
required to balance the exploration of search space and exploitation of optimal
solution of the ABC. This paper outlines a new hybrid of ABC algorithm with
Genetic Algorithm. The proposed method integrates crossover operation from
Genetic Algorithm (GA) with original ABC algorithm. The proposed method is
named as Crossover based ABC (CbABC). The CbABC strengthens the exploitation
phase of ABC as crossover enhances exploration of search space. The CbABC
tested over four standard benchmark functions and a popular continuous
optimization problem.
