Expedited Artificial Bee Colony Algorithm

Abstract

Artificial Bee Colony (ABC) is one of the latest and emerging swarm intelligence algorithms. Though, there are some areas where ABC works better than other optimization techniques but, the drawbacks like stucking at local optima and preferring exploration at the cost of exploitation, are also associated with it. This paper uses position update equation in ABC as in Gbest-guided ABC (GABC) and attempts to improve ABC algorithm by balancing its exploration and exploitation capabilities. The proposed algorithm is named as Expedited Artificial Bee Colony (EABC). We altered the onlooker bee phase of ABC by forcing the individual bee to take positive direction towards the random bee if this selected random bee has better fitness than the current bee and if it is not the case then the current bee will move in reverse direction. In this way, ABC colony members will not follow only global best bee but also a random bee which has better fitness than the current bee which is going to be modified. So the mentioned drawbacks of the ABC may be resolved. To analyze the performance of the proposed modification, 14 unbiased benchmark optimization functions have been considered and experimental results reflect its superiority over the Basic ABC and GABC.