An improved GA and a novel PSO-GA-based hybrid algorithm

被引：259
|
作者：
Shi, XH
Liang, YC [1 ]
Lee, HP
Lu, C
Wang, LM
机构：
[1] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China
[2] Inst High Performance Comp, Singapore 117528, Singapore
[3] Changchun Taxat Coll, Dept Comp Sci & Technol, Changchun 130021, Peoples R China
来源：
INFORMATION PROCESSING LETTERS | 2005年 / 93卷 / 05期
基金：
中国国家自然科学基金;
关键词：
algorithms; genetic algorithms; particle swarm optimization; hybrid evolutionary algorithms; optimization;
D O I：
10.1016/j.ipl.2004.11.003
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Inspired by the natural features of the variable size of the population, we present a variable population-size genetic algorithm (VPGA) by introducing the "dying probability" for the individuals and the "war/disease process" for the population. Based on the VPGA and the particle swarm optimization (PSO) algorithms, a novel PSO-GA-based hybrid algorithm (PGHA) is also proposed in this paper. Simulation results show that both VPGA and PGHA are effective for the optimization problems. (C) 2004 Elsevier B.V. All rights reserved.
引用
收藏
页码：255 / 261
页数：7
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