TY - GEN
T1 - Hybrid particle swarm and conjugate gradient optimization algorithm
AU - Qteish, Abdallah
AU - Hamdan, Mohammad
PY - 2010
Y1 - 2010
N2 - In this work we propose a different particle swarm optimization (PSO) algorithm that employs two key features of the conjugate gradient (CG) method. Namely, adaptive weight factor for each particle and iteration number (calculated as in the CG approach), and periodic restart. Experimental results for four well known test problems have showed the superiority of the new PSO-CG approach, compared with the classical PSO algorithm, in terms of convergence speed and quality of obtained solutions.
AB - In this work we propose a different particle swarm optimization (PSO) algorithm that employs two key features of the conjugate gradient (CG) method. Namely, adaptive weight factor for each particle and iteration number (calculated as in the CG approach), and periodic restart. Experimental results for four well known test problems have showed the superiority of the new PSO-CG approach, compared with the classical PSO algorithm, in terms of convergence speed and quality of obtained solutions.
UR - http://www.scopus.com/inward/record.url?scp=77954647477&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-13495-1_71
DO - 10.1007/978-3-642-13495-1_71
M3 - Conference contribution
AN - SCOPUS:77954647477
SN - 9783642134944
T3 - Lecture Notes in Computer Science
SP - 582
EP - 588
BT - Advances in Swarm Intelligence. ICSI 2010
PB - Springer
T2 - 1st International Conference on Advances in Swarm Intelligence 2010
Y2 - 12 June 2010 through 15 June 2010
ER -