Convergence Analysis of Swarm Algorithm

被引：0

作者：

Liu, Hongbo ^{[1
,2
,4
]}

Abraham, Ajith ^{[1
,4
]}

Snasel, Vaclav ^{[3
,4
]}

机构：

[1] Dalian Maritime Univ, Sch Informat Sci & Technol, Dalian 026, Peoples R China

[2] Dalian Univ Technol, Dept Comp Sci, Dalian 116023, Peoples R China

[3] VSB Tech Univ Ostrava, Dept Comp Sci, Ostrava 70833, Czech Republic

[4] Machine Intelligence Res Labs MIR, Auburn, NY 98071 USA

来源：

2009 WORLD CONGRESS ON NATURE & BIOLOGICALLY INSPIRED COMPUTING (NABIC 2009) | 2009年

关键词：

PARTICLE SWARM; OPTIMIZATION;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Swarm Intelligence (SI) is an innovative distributed intelligent paradigm whereby the collective behaviors of unsophisticated individuals interacting locally with their environment cause coherent functional global patterns to emerge. Although the swarm algorithms have exhibited good performance across a wide range of application problems, it is difficult to analyze the convergence. We discuss the swarm intelligent model namely the particle swarm based on its iterated function system. The dynamic trajectory of the particle is described based single individual. We also attempt to theoretically prove that the swarm algorithm converges with a probability of I towards the global optimal.

引用

页码：1713 / +

页数：2

共 27 条

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