|Communications on Applied Electronics
|Foundation of Computer Science (FCS), NY, USA
|Volume 5 - Number 8
|Year of Publication: 2016
|Authors: Shifat Sharmin Shapla, Tanveer Ahmed Belal, Mohammad Shafiul Alam
Shifat Sharmin Shapla, Tanveer Ahmed Belal, Mohammad Shafiul Alam . An Empirical Comparison between the Artificial Bee Colony and Bat Algorithms on Continuous Function Optimization Problem. Communications on Applied Electronics. 5, 8 ( Aug 2016), 24-28. DOI=10.5120/cae2016652347
Swarm intelligence is the collective and collaborative behavior of self-organized systems, natural or artificial. Swarm intelligence algorithms basically come from nature or biological behavior of nature. In this paper we have conducted an experimental comparison between two Swarm intelligence algorithms — the Artificial Bee Colony (ABC) algorithm and basic Bat algorithm on both unimodal and multimodal high dimensional continuous functions. The ABC algorithm has a well-balanced exploration and exploitation ability which is based on the intelligent food foraging behavior of honey bee swarm, proposed by Karaboga in the year 2005, while the Bat algorithm was inspired by the echolocation behavior of bats found in nature. The experimental results show that the ABC algorithm performs better than the BAT algorithm.