Title :
Good Lattice Points-Based Particle Swarm Optimizer
Author :
Su, Shoubao ; Wang, Jiwen
Author_Institution :
Anhui Univ., Hefei
Abstract :
The mechanism of the classical particle swarm optimization and the comparison criterion of different natural computing methods is investigated by introducing the discrepancy and good lattice points in number theory and proposes a novel optimization method, called good lattice points-based particle swarm optimization algorithm, which intends to produce faster and more accurate convergence because it has a solid theoretical basis and better global search ability, meanwhile the global convergence of the presented algorithm with asymptotic probability one is proved by the property of the optimal lattice. Finally experiment results are very promising to illustrate the outstanding feature of the presented algorithm.
Keywords :
convergence; lattice theory; number theory; particle swarm optimisation; probability; search problems; asymptotic probability; global convergence; global search ability; good lattice points; natural computing methods; number theory; optimal lattice; particle swarm optimization; Algorithm design and analysis; Ant colony optimization; Computer science; Computer science education; Convergence; Lattices; Optimization methods; Particle swarm optimization; Signal processing; Signal processing algorithms;
Conference_Titel :
Natural Computation, 2007. ICNC 2007. Third International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2875-5
DOI :
10.1109/ICNC.2007.408