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Enhanced constrained artificial bee colony algorithm for optimization problems

Babaeizadeh, Soudeh and Ahmad, Rohanin (2017) Enhanced constrained artificial bee colony algorithm for optimization problems. International Arab Journal of Information Technology, 14 (2). pp. 246-253. ISSN 1683-3198

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Abstract

Artificial Bee Colony (ABC) algorithm is a relatively new swarm intelligence algorithm that has attracted great deal of attention from researchers in recent years with the advantage of less control parameters and strong global optimization ability. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. This drawback can be even more significant when constraints are also involved. To address this issue, an Enhanced Constrained ABC algorithm (EC-ABC) is proposed for Constrained Optimization Problems (COPs) where two new solution search equations are introduced for employed bee and onlooker bee phases respectively. In addition, both chaotic search method and opposition-based learning mechanism are employed to be used in population initialization in order to enhance the global convergence when producing initial population. This algorithm is tested on several benchmark functions where the numerical results demonstrate that the EC-ABC is competitive with state of the art constrained ABC algorithm.

Item Type:Article
Additional Information:RADIS System Ref No:PB/2017/11525
Uncontrolled Keywords:search equation, swarm intelligence
Subjects:Q Science
Divisions:Science
ID Code:66170
Deposited By: Widya Wahid
Deposited On:13 Jul 2017 15:18
Last Modified:13 Jul 2017 15:18

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