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ABC-T: Modified Artificial Bee Colony Algorithm with Parameter Tuning for Continuous Function Optimization

Sadman Sakib, Mahzabeen Emu in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication: 2018
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors:Sadman Sakib, Mahzabeen Emu
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  1. Sadman Sakib and Mahzabeen Emu. ABC-T: Modified Artificial Bee Colony Algorithm with Parameter Tuning for Continuous Function Optimization. International Journal of Applied Information Systems 12(17):1-7, December 2018. URL, DOI BibTeX

    	author = "Sadman Sakib and Mahzabeen Emu",
    	title = "ABC-T: Modified Artificial Bee Colony Algorithm with Parameter Tuning for Continuous Function Optimization",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "December, 2018",
    	volume = 12,
    	number = 17,
    	month = "December",
    	year = 2018,
    	issn = "2249-0868",
    	pages = "1-7",
    	url = "",
    	doi = "10.5120/ijais2018451781",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"


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.


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Swarm intelligence algorithm; Artificial Bee Colony algorithm; Exploitation; Exploration; Unimodal function