TY - GEN
T1 - Generalized opposition-based artificial bee colony algorithm
AU - El-Abd, Mohammed
PY - 2012
Y1 - 2012
N2 - The Artificial Bee Colony (ABC) algorithm is a relatively new algorithm for function optimization. The algorithm is inspired by the foraging behavior of honey bees. In this work, the performance of ABC is enhanced by introducing the concept of generalized opposition-based learning. This concept is introduced through the initialization step and through generation jumping. The performance of the proposed generalized opposition-based ABC (GOABC) is compared to the performance of ABC and opposition-based ABC (OABC) using the CEC05 benchmarks library.
AB - The Artificial Bee Colony (ABC) algorithm is a relatively new algorithm for function optimization. The algorithm is inspired by the foraging behavior of honey bees. In this work, the performance of ABC is enhanced by introducing the concept of generalized opposition-based learning. This concept is introduced through the initialization step and through generation jumping. The performance of the proposed generalized opposition-based ABC (GOABC) is compared to the performance of ABC and opposition-based ABC (OABC) using the CEC05 benchmarks library.
UR - http://www.scopus.com/inward/record.url?scp=84866848302&partnerID=8YFLogxK
U2 - 10.1109/CEC.2012.6252939
DO - 10.1109/CEC.2012.6252939
M3 - Conference contribution
SN - 9781467315098
T3 - 2012 IEEE Congress on Evolutionary Computation, CEC 2012
SP - 3046
EP - 3049
BT - 2012 IEEE Congress on Evolutionary Computation, CEC 2012
T2 - 2012 IEEE Congress on Evolutionary Computation, CEC 2012
Y2 - 10 June 2012 through 15 June 2012
ER -