An improved artificial bee colony algorithm based on mean best-guided approach for continuous optimization problems and real brain MRI images segmentation

2020 ◽  
Author(s):  
Ayat Alrosan ◽  
Waleed Alomoush ◽  
Norita Norwawi ◽  
Mohammed Alswaitti ◽  
Sharif Naser Makhadmeh
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Brain Mri ◽  
Continuous Optimization ◽  
Bee Colony ◽  
Continuous Optimization Problems

scholarly journals A Modified Artificial Bee Colony Algorithm with Firefly Algorithm Strategy for Continuous Optimization Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Amnat Panniem ◽  
Pikul Puphasuk
Keyword(s):  
Firefly Algorithm ◽  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Control Parameters ◽  
Exploration And Exploitation ◽  
Bee Colony ◽  
Continuous Problems ◽  
Local Solutions ◽  
Continuous Optimization Problems

Artificial Bee Colony (ABC) algorithm is one of the efficient nature-inspired optimization algorithms for solving continuous problems. It has no sensitive control parameters and has been shown to be competitive with other well-known algorithms. However, the slow convergence, premature convergence, and being trapped within the local solutions may occur during the search. In this paper, we propose a new Modified Artificial Bee Colony (MABC) algorithm to overcome these problems. All phases of ABC are determined for improving the exploration and exploitation processes. We use a new search equation in employed bee phase, increase the probabilities for onlooker bees to find better positions, and replace some worst positions by the new ones in onlooker bee phase. Moreover, we use the Firefly algorithm strategy to generate a new position replacing an unupdated position in scout bee phase. Its performance is tested on selected benchmark functions. Experimental results show that MABC is more effective than ABC and some other modifications of ABC.

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