Title :
Hybrid Ensemble Particle Swarm Optimization
Author_Institution :
Sch. of Comput. & Inf. Eng., Beijing Technol. & Bus. Univ., Beijing, China
Abstract :
In this paper a hybrid ensemble particle swarm optimization (HEPSO) algorithm is presented. It combines ensemble learning, subpopulation, part dimensions and random order strategies together. Ensemble learning can help providing a more accurate global guider through combining some previous best positions (pbest) of the particles. The other three strategies increase the diversity. And this algorithm is compared with standard PSO and some other improved PSO to illustrate how HPSO can benefit from these strategies.
Keywords :
learning (artificial intelligence); particle swarm optimisation; diversity; ensemble learning; hybrid ensemble particle swarm optimization algorithm; part dimensions strategy; particle best position; random order strategy; subpopulation; Clamps; Diversity reception; Equations; Genetic mutations; Greedy algorithms; History; Particle swarm optimization; ensemble learning; hybrid strategies; particle swarm optimization;
Conference_Titel :
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3736-8
DOI :
10.1109/ICNC.2009.565
