International Journal of Applied Information Systems |
Foundation of Computer Science (FCS), NY, USA |
Volume 12 - Number 17 |
Year of Publication: 2018 |
Authors: Sadman Sakib, Mahzabeen Emu |
10.5120/ijais2018451781 |
Sadman Sakib, Mahzabeen Emu . ABC-T: Modified Artificial Bee Colony Algorithm with Parameter Tuning for Continuous Function Optimization. International Journal of Applied Information Systems. 12, 17 ( December 2018), 1-7. DOI=10.5120/ijais2018451781
This paper carries a comparative study on a population-based swarm intelligence (SI) algorithm and improved modified version of that algorithm. For optimization problems, the nature-inspired algorithm works better than other algorithms. There are different types of swarm intelligence algorithms available for this purpose. Among these swarm intelligence algorithms, ABC algorithm is one algorithm where 3 types of bees are seen, employed bees, onlooker bees, scout bees. Employed bees and scout bee are responsible for exploration whereas onlooker bees are responsible for exploitation. A modified version of ABC (Artificial Bee Colony) has been implemented and then compared with the standard ABC algorithm. The comparisons are conducted on an experimental set of eleven benchmark functions. The modified version of ABC that is proposed is named ABC with tuning (ABC-T). In our analysis, the rate of exploitation and exploration was changed by maintaining one static and five dynamic ratios of employed and onlooker bees to find out which combination performs well and which combination does not perform notably. The results produced by ABC-T with different ratio of exploration and exploitation is also compared to each other to find out which combination performs better for which type of function.