A survey of hybridization of abc for optimization of continuous functioning

Swarm intelligence algorithms are meta heuristics that simulates the nature for solving optimization problems, Artificial Bee Colony (ABC) algorithm is one of the most recent nature inspired algorithms which used for problem optimization, numerous research efforts has been concentrated in this particular area. However, the Artificial Bee Colony performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The Artificial Bee Colony is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions, in order to overcome these limitations as well as to broaden the scope and viability of nature inspired algorithms many variations of this algorithm are being presented and the results being very amazing. This paper presents an overview of some of the hybridized meta heuristics with Artificial Bee Colony algorithm for continuous function optimization; many benchmark functions have been used to show the validity of every approach.