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An Empirical Comparison between the Artificial Bee Colony and Bat Algorithms on Continuous Function Optimization Problem

Shifat Sharmin Shapla, Tanveer Ahmed Belal, Mohammad Shafiul Alam. Published in Algorithms.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Shifat Sharmin Shapla, Tanveer Ahmed Belal, Mohammad Shafiul Alam
10.5120/cae2016652347

Shifat Sharmin Shapla, Tanveer Ahmed Belal and 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):24-28, August 2016. BibTeX

@article{10.5120/cae2016652347,
	author = {Shifat Sharmin Shapla and Tanveer Ahmed Belal and Mohammad Shafiul Alam},
	title = {An Empirical Comparison between the Artificial Bee Colony and Bat Algorithms on Continuous Function Optimization Problem},
	journal = {Communications on Applied Electronics},
	issue_date = {August 2016},
	volume = {5},
	number = {8},
	month = {Aug},
	year = {2016},
	issn = {2394-4714},
	pages = {24-28},
	numpages = {5},
	url = {http://www.caeaccess.org/archives/volume5/number8/640-2016652347},
	doi = {10.5120/cae2016652347},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

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.

References

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Keywords

Swarm intelligence, artificial bee colony algorithm, bat algorithm, continuous function optimization.