Artificial Bee Colony (ABC) algorithm is the most popular add-on to class of swarm intelligence based meta-heuristic which is evolved to resolve the complex real world optimization problems. Most of the swarm intelligence based algorithms face the problem of stagnation, and premature convergence and ABC is not an exception. To reduce the chance of these problems as well as to control equilibrium between intensification and diversification capabilities of ABC, a unique variant of ABC is intended. In this intended variant, the employed bee stage, as well as onlooker bee stage of ABC algorithm is modified by taking inspiration from a local best candidate as well as the global best candidate. The intended ABC variant is named as Lbest Gbest ABC (LGABC) algorithm. The accuracy and efficiency of LGABC have examined over 12 benchmark functions and evaluated with the basic ABC, best so far ABC, Gbest ABC and Modified ABC and found that it may be an efficient contender in the field of swarm intelligence based algorithms.