Title :
Stereotyping: improving particle swarm performance with cluster analysis
Author_Institution :
Bur. of Labor Stat., Washington, DC, USA
Abstract :
Individuals in the particle swarm population were “stereotyped” by cluster analysis of their previous best positions. The cluster centers then were substituted for the individuals´ and neighbors´ best previous positions in the algorithm. The experiments, which were inspired by the social-psychological metaphor of social stereotyping, found that performance could be generally improved by substituting individuals´, but not neighbors´, cluster centers for their previous bests
Keywords :
pattern recognition; stereo image processing; cluster analysis; particle swarm performance; social-psychological metaphor; stereotyping; Acceleration; Clustering algorithms; Collaboration; Humans; Optimization methods; Particle swarm optimization; Performance analysis; Prototypes; Psychology; Statistical analysis;
Conference_Titel :
Evolutionary Computation, 2000. Proceedings of the 2000 Congress on
Conference_Location :
La Jolla, CA
Print_ISBN :
0-7803-6375-2
DOI :
10.1109/CEC.2000.870832
