Survey on Improved Artificial Bee Colony Algorithm for Balancing Exploration and Exploitation

Today many fields like engineering, management, and economy faces many optimization problems which are needed to be solved by meta-heuristics techniques. The Artificial Bee Colony (ABC) algorithm is a population-based stochastic swarm intelligence algorithm finds applications in most of the fields. The problem with ABC is its slow convergence speed due to the poor exploitation capability and falls into local minima in the case of multimodal functions. The efficiency of the standard ABC lies in the balance between the exploration and exploitation level since ABC is good at exploration. Researchers are working on ABC by applying new techniques in each phase to increase the capability of the algorithm. Many improved ABC algorithms are proposed, mostly the improvement is found in onlooker or scout bee phase, in the literature each with a novel, hybrid techniques to increase the performance of the algorithm. This paper explores the work carried out in the literature to provide the good knowledge of improving the algorithm to attain the best search