IMPROVING GLOBAL OPTIMIZATION ABILITY OF GSO USING ENSEMBLE LEARNING

被引：0

作者：

Wang, Qin ^{[1
]}

Shi, Yan ^{[2
]}

Zeng, Guangping ^{[1
]}

Tu, Xuyan ^{[1
]}

机构：

[1] Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Beijing 100083, Peoples R China

[2] Beijing Technol & Business Univ, Sch Comp & Informat Engn, Beijing 100048, Peoples R China

来源：

2012 IEEE 2ND INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND INTELLIGENT SYSTEMS (CCIS) VOLS 1-3 | 2012年

关键词：

Swarm intelligence; Glowworm swarm optimization (GSO); Ensemble learning method;

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：

As a novel bionic swarm intelligence optimization method, Glowworm Swarm Optimization (GSO) algorithm is inspired by the social behavior of glowworm and the phenomenon of bioluminescent communication, but GSO is easy to fall into local optimization point, and has the low speed of convergence in the late. In order to solve these problems, a method GSOE, combined with the GSO and the ensemble learning method, is presented. Through 4 typical functions testing, experiment results show that the method offers an effective way to avoid local optimization, and can improve the optimization global ability obviously.

引用

页码：118 / 121

页数：4

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