Title :
The hybrid implementation genetic algorithm with particle swarm optimization to solve the unconstrained optimization problems
Author :
Nootyaskool, Supakit
Author_Institution :
Fac. of Inf. Technol., King Mongkut´´s Inst. of Technol. Ladkrabang, Bangkok, Thailand
Abstract :
Genetic algorithm (GA) has an advantage in exploration search. Particle swarm optimization (PSO) has an advantage in sharing movement information between particles. The combining between GA and PSO is proposed in this research. We design hybrid-GA with PSO, and compare the performance with simple GA and simple PSO, which their models will find the solution of five-difference complexity of numerical functions. The experiment result showed that hybrid GA with PSO can find the solution of a multimodal problem and unimodal with noise signal quickly.
Keywords :
genetic algorithms; particle swarm optimisation; search problems; GA; PSO; exploration search; five-difference complexity; genetic algorithm; hybrid implementation genetic algorithm; multimodal problem; numerical functions; particle swarm optimization; unconstrained optimization problems; unimodal problem; IP networks; Noise; Numerical models; genetic algorithm; hybrid technique; numerical optimization; particle swarm optimization;
Conference_Titel :
Knowledge and Smart Technology (KST), 2012 4th International Conference on
Conference_Location :
Chonburi
Print_ISBN :
978-1-4673-2166-2
DOI :
10.1109/KST.2012.6287739