TY - GEN
T1 - Local best artificial bee colony algorithm with dynamic sub-populations
AU - El-Abd, Mohammed
PY - 2013
Y1 - 2013
N2 - The Artificial Bee Colony (ABC) algorithm is a powerful continuous optimization tool that has been proposed in the past few years. Many studies have shown the superior performance of ABC when compared to other well-known optimization algorithms. In this paper, the implementation of an ABC algorithm with dynamic sub-populations (ABCDP) is presented. The algorithm is compared against a number of previously proposed ABC algorithms guided by global-best information. The comparison is based on the final solution reached, robustness, and number of successfully solved functions for all the algorithms when applied to the well-known CEC05 benchmark functions.
AB - The Artificial Bee Colony (ABC) algorithm is a powerful continuous optimization tool that has been proposed in the past few years. Many studies have shown the superior performance of ABC when compared to other well-known optimization algorithms. In this paper, the implementation of an ABC algorithm with dynamic sub-populations (ABCDP) is presented. The algorithm is compared against a number of previously proposed ABC algorithms guided by global-best information. The comparison is based on the final solution reached, robustness, and number of successfully solved functions for all the algorithms when applied to the well-known CEC05 benchmark functions.
UR - http://www.scopus.com/inward/record.url?scp=84881599089&partnerID=8YFLogxK
U2 - 10.1109/CEC.2013.6557613
DO - 10.1109/CEC.2013.6557613
M3 - Conference contribution
AN - SCOPUS:84881599089
SN - 9781479904549
T3 - 2013 IEEE Congress on Evolutionary Computation, CEC 2013
SP - 522
EP - 528
BT - 2013 IEEE Congress on Evolutionary Computation, CEC 2013
T2 - 2013 IEEE Congress on Evolutionary Computation, CEC 2013
Y2 - 20 June 2013 through 23 June 2013
ER -