Title :
A Multi-Subpopulation Accelerating Particle Swarm Optimization
Author_Institution :
Wuhan Univ., Wuhan
Abstract :
The particle swarm optimization is a stochastic, population-based optimization technique that can be applied to a wide range of problems. A multi- subpopulation accelerating particle swarm optimization(MAPSO)is proposed to improve the performance of the original algorithm. MAPSO views the excellent individuals as attractors and generates local small populations in the neighbor of them to maintain the diversity of the population. In the course of searching, MAPSO constantly shrinks the searching neighbor and uses the accelerating operators to speed up the evolution of MAPSO. Finally, MAPSO´s efficiency is validated through optimization of benchmark functions.
Keywords :
particle swarm optimisation; stochastic processes; multisubpopulation accelerating particle swarm optimization; population-based optimization technique; Acceleration; Birds; Computational intelligence; Computer science; Data mining; Evolutionary computation; Particle swarm optimization; Random sequences; Stochastic processes; Testing;
Conference_Titel :
Knowledge Discovery and Data Mining, 2008. WKDD 2008. First International Workshop on
Conference_Location :
Adelaide, SA
Print_ISBN :
978-0-7695-3090-1
DOI :
10.1109/WKDD.2008.69