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

X. H. Shi; Y. C. Liang; H. P. Lee; C. Lu; L. M. Wang

doi:10.1016/j.ipl.2004.11.003

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

X. H. Shi
, Y. C. Liang^*
, H. P. Lee
, C. Lu
, L. M. Wang

^*Corresponding author for this work

College of Computer Science and Technology
Agency for Science, Technology and Research, Singapore
Jilin University of Finance and Economics

Research output: Contribution to journal › Article › peer-review

365 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	255-261
Number of pages	7
Journal	Information Processing Letters
Volume	93
Issue number	5
DOIs	https://doi.org/10.1016/j.ipl.2004.11.003
State	Published - 16 Mar 2005
Externally published	Yes

Keywords

Algorithms
Genetic algorithms
Hybrid evolutionary algorithms
Optimization
Particle swarm optimization

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10.1016/j.ipl.2004.11.003

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title = "An improved GA and a novel PSO-GA-based hybrid algorithm",

abstract = "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.",

keywords = "Algorithms, Genetic algorithms, Hybrid evolutionary algorithms, Optimization, Particle swarm optimization",

author = "Shi, \{X. H.\} and Liang, \{Y. C.\} and Lee, \{H. P.\} and C. Lu and Wang, \{L. M.\}",

year = "2005",

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doi = "10.1016/j.ipl.2004.11.003",

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T1 - An improved GA and a novel PSO-GA-based hybrid algorithm

AU - Shi, X. H.

AU - Liang, Y. C.

AU - Lee, H. P.

AU - Lu, C.

AU - Wang, L. M.

PY - 2005/3/16

Y1 - 2005/3/16

N2 - 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.

AB - 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.

KW - Algorithms

KW - Genetic algorithms

KW - Hybrid evolutionary algorithms

KW - Optimization

KW - Particle swarm optimization

UR - https://www.scopus.com/pages/publications/12244256802

U2 - 10.1016/j.ipl.2004.11.003

DO - 10.1016/j.ipl.2004.11.003

M3 - 文章

AN - SCOPUS:12244256802

SN - 0020-0190

VL - 93

SP - 255

EP - 261

JO - Information Processing Letters

JF - Information Processing Letters

IS - 5

ER -

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

Abstract

Keywords

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