Title :
Dynamic multi-swarm particle swarm optimizer with local search for Large Scale Global Optimization
Author :
Zhao, S.Z. ; Liang, J.J. ; Suganthan, P.N. ; Tasgetiren, M.F.
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore
Abstract :
In this paper, the performance of dynamic multi-swarm particle swarm optimizer (DMS-PSO) on the set of benchmark functions provided for the CEC2008 Special Session on Large Scale optimization is reported. Different from the existing multi-swarm PSOs and local versions of PSO, the sub-swarms are dynamic and the sub-swarmspsila size is very small. The whole population is divided into a large number sub-swarms, these sub-swarms are regrouped frequently by using various regrouping schedules and information is exchanged among the particles in the whole swarm. The Quasi-Newton method is combined to improve its local searching ability.
Keywords :
Newton method; particle swarm optimisation; search problems; dynamic multi-swarm particle swarm optimizer; large scale global optimization; local search; quasi-newton method; regrouping schedules; sub-swarms; Acceleration; Animals; Birds; Business communication; Constraint optimization; Engineering management; Large-scale systems; Particle swarm optimization; Statistics; Technology management;
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
DOI :
10.1109/CEC.2008.4631320
