Self-Adaptive and Adaptive Parameter Control in Improved Artificial Bee Colony Algorithm

Afşar, Bekir; Aydin, Doğan; Uğur, Aybars; Korukoğlu, Serdar

doi:10.15388/Informatica.2017.136

Informatica

Self-Adaptive and Adaptive Parameter Control in Improved Artificial Bee Colony Algorithm

Volume 28, Issue 3 (2017), pp. 415–438

Bekir Afşar Doğan Aydin Aybars Uğur Serdar Korukoğlu

https://doi.org/10.15388/Informatica.2017.136

Pub. online: 1 January 2017 Type: Research Article

Open Access

Received
1 June 2015

Accepted
1 September 2016

Published
1 January 2017

Abstract

The Improved Artificial Bee Colony (IABC) algorithm is a variant of the well-known Artificial Bee Colony (ABC) algorithm. In IABC, a new initialization approach and a new search mechanism were added to the ABC for avoiding local optimums and a better convergence speed. New parameters were added for the new search mechanism. Specified values of these newly added parameters have a direct impact on the performance of the IABC algorithm. For better performance of the algorithm, parameter values should be subjected to change from problem to problem and also need to be updated during the run of the algorithm. In this paper, two novel parameter control methods and related algorithms have been developed in order to increase the performance of the IABC algorithm for large scale optimization problems. One of them is an adaptive parameter control which updates parameter values according to the feedback coming from the search process during the run of the algorithm. In the second method, the management of the parameter values is left to the algorithm itself, which is called self-adaptive parameter control. The adaptive IABC algorithms were examined and compared to other ABC variants and state-of-the-art algorithms on a benchmark functions suite. Through the analysis of the results of the experiments, the adaptive IABC algorithms outperformed almost all ABC variants and gave competitive results with state-of-the-art algorithms from the literature.

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Biographies

Afşar Bekir

bekirafsar@gmail.com

B. Afşar is an independent researcher. Previously, he was research assistant in Department of Computer Engineering at Muğla Sıtkı Koçman University, Muğla. He received the PhD degree in computer engineering at Ege University, Izmir. His research interests are in the areas of metaheuristics, continuous optimization, self-adaptive approaches and model-driven software development. He has published conference papers in area of model-driven development and metaheuristics.

Aydin Doğan

dogan.aydin@dpu.edu.tr

D. Aydın is an associate professor of computer engineering at Dumlupınar University, Kütahya. He was also a visiting researcher in IRIDIA at Universite Libr de Bruxelles, Brussels. He received the PhD degree in computer engineering at Ege University, Izmir. He is guest editor of two international journals and referee in several high-impact scientific journals in the frame of artificial intelligence and energy. He has published more than 30 papers in journals and conferences. His main research interests are: metaheuristics, continuous optimization, swarm intelligence, automatic parameter configuration and image processing.

Uğur Aybars

aybars.ugur@ege.edu.tr

A. Uğur is a full-time professor in the Department of Computer Engineering at Ege University, Izmir, Turkey. He received his BS, MSc and PhD degrees in computer engineering from Ege University, Izmir, Turkey, in 1993, 1996, 2001, respectively. His research interests are artificial intelligence, swarm intelligence, optimization, intelligent systems, computer vision and computer graphics.

Korukoğlu Serdar

serdar.korukoglu@ege.edu.tr

S. Korukoğlu is a full-time professor in the Department of Computer Engineering at Ege University, Izmir, Turkey. He received his BS degree in Industrial Engineering, MSc in Applied Statistics and PhD in computer engineering from Ege University, Izmir, Turkey, in 1978, 1980 and 1984, respectively. He was in Reading University of England as a visiting research fellow in 1985. His research interests include discrete-event simulation, statistical analysis, optimization techniques and algorithms, and applied computing.

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Keywords

Artificial Bee Colony Improved Artificial Bee Colony parameter control methods adaptive parameter control self-adaptive parameter control

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