TY - GEN
T1 - A hybrid ABC-SPSO algorithm for continuous function optimization
AU - El-Abd, Mohammed
PY - 2011
Y1 - 2011
N2 - In this paper we investigate the hybridization of two swarm intelligence algorithms; namely, the Artificial Bee Colony Algorithm (ABC) and Particle Swarm Optimization (PSO). The hybridization technique is a component-based one where the PSO algorithm is augmented with an ABC component to improve the personal bests of the particles. Two different hybrid algorithms are tested in this work based on the method in which the ABC component is applied to the different particles. All the algorithms are applied to the well-known CEC05 benchmark functions and compared based on three different metrics.
AB - In this paper we investigate the hybridization of two swarm intelligence algorithms; namely, the Artificial Bee Colony Algorithm (ABC) and Particle Swarm Optimization (PSO). The hybridization technique is a component-based one where the PSO algorithm is augmented with an ABC component to improve the personal bests of the particles. Two different hybrid algorithms are tested in this work based on the method in which the ABC component is applied to the different particles. All the algorithms are applied to the well-known CEC05 benchmark functions and compared based on three different metrics.
UR - http://www.scopus.com/inward/record.url?scp=79961164467&partnerID=8YFLogxK
U2 - 10.1109/SIS.2011.5952576
DO - 10.1109/SIS.2011.5952576
M3 - Conference contribution
AN - SCOPUS:79961164467
SN - 9781612840529
T3 - IEEE SSCI 2011 - Symposium Series on Computational Intelligence - SIS 2011: 2011 IEEE Symposium on Swarm Intelligence
SP - 96
EP - 101
BT - IEEE SSCI 2011 - Symposium Series on Computational Intelligence - SIS 2011
T2 - Symposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE Symposium on Swarm Intelligence, SIS 2011
Y2 - 11 April 2011 through 15 April 2011
ER -